1. A computer-implemented unseen wildlife species identification system (100), the system (100) comprising: a computing unit (102) comprising a processor and a non-transitory memory storing machine-readable instructions executable by the processor; an image input module (104) configured to receive wildlife images captured from camera-trap devices, drones, or image acquisition sensors: a pre-processing module (106) configured to perform pre-processing including resizing, normalization, and enhancement to generate visual input data; a semantic attribute module (108) configured to receive textual descriptions, attribute vectors, or ontology-based representations associated with animal species and to encode the textual descriptions into semantic feature embeddings; an environmental context module (110) configured to collect contextual metadata associated with an observation event and to encode the contextual metadata into environmental feature vectors; a modality-specific encoder (112) configured to transform the visual input data, semantic feature embeddings, and environmental feature vectors into corresponding latent representations within a common embedding space; an adaptive semantic attribute fusion module (114) comprising a context-aware weighting network configured to dynamically assign modality weights to the latent representations based on contextual conditions; a cross-domain alignment module (116) configured to align representations of known species and candidate unseen species within the common embedding space using prototype representations derived from semantic attributes and learned feature distributions; and an output module (118) configured to determine a predicted species identity for an observed animal instance by computing similarity between the fused embedding and species prototypes corresponding to known and unseen species.

2. The system (100) as claimed in claim 1, wherein the modality-specific encoder (112) comprises a visual encoder implemented using a convolutional neural network architecture and semantic and environmental encoders implemented using multilayer perceptrons or transformer-based networks.

3. The system (100) as claimed in claim 1, wherein the adaptive semantic attribute fusion module (114) further comprises projection heads configured to transform visual, semantic, and environmental representations into modality-specific embeddings prior to fusion.

4. The system (100) as claimed in claim 1, wherein the cross-domain alignment module (116) further comprises a domain discrepancy minimization mechanism configured to reduce distribution differences between visual and semantic feature domains.

5. The system (100) as claimed in claim 1, wherein the image input module (104) is further configured to receive image data from a plurality of distributed wildlife monitoring devices including camera traps, unmanned aerial vehicles, and stationary environmental monitoring sensors deployed in natural habitats.

6. The system (100) as claimed in claim 1, wherein the pre-processing module (106) is further configured to perform image enhancement operations comprising noise reduction, contrast adjustment, deblurring, and data normalization in order to improve feature extraction accuracy.

7. The system (100) as claimed in claim 1, wherein the semantic attribute module (108) is configured to generate semantic embeddings using a pre-trained language model, attribute embedding network, or ontology-based encoding framework that converts species descriptions into fixed-length semantic vectors.

8. The system (100) as claimed in claim 1, wherein the semantic attribute module (108) is further configured to process species attribute information comprising at least one of body color, body size, texture pattern, anatomical structure, behavioral characteristics, or ecological habitat information.

9. The system (100) as claimed in claim 1, wherein the environmental context module (110) is configured to collect contextual metadata comprising at least one of geographic location coordinates, elevation, temperature, humidity, time of day, season, habitat classification, and weather conditions.

10. The system (100) as claimed in claim 1, wherein the environmental context module (110) is further configured to encode discrete contextual variables using embedding layers and continuous contextual variables using normalization functions to produce environmental feature vectors.

Description:FIELD OF DISCLOSURE
[0001] The present disclosure relates generally relates to the field of Artificial Intelligence and Computer Vision. More specifically, it pertains to a computer-implemented unseen wildlife species identification system using an Adaptive Semantic Attribute Fusion Network (ASAF-Net).
BACKGROUND OF THE DISCLOSURE
[0002] The field of wildlife monitoring and biodiversity conservation increasingly relies on automated image and data analysis systems to identify and classify animal species from visual observations. With the growing availability of camera traps, aerial monitoring devices, and ecological sensing technologies, large volumes of wildlife imagery and contextual data are continuously generated. However, identifying species at a fine-grained level remains a complex task, particularly when the available data is limited or when the observed species belong to rare, endangered, or previously unseen categories. Conventional species recognition systems typically depend on supervised machine learning approaches that require extensive labeled datasets for training. In practical ecological scenarios, collecting and manually annotating such datasets is expensive, labor-intensive, and often impractical, especially for species that are rarely encountered in the wild.
[0003] In many wildlife monitoring programs, researchers encounter situations in which new or rarely documented species appear in captured images or videos. Traditional classification algorithms struggle to recognize such unseen classes because the models are trained only on predefined categories. This limitation restricts the applicability of conventional supervised learning models in biodiversity studies where the diversity of species is vast and continuously evolving. Additionally, field conditions such as lighting variations, seasonal changes, occlusion, and habitat diversity can significantly affect the visual appearance of animals, further complicating accurate classification. These environmental variations make it difficult for models trained under controlled datasets to generalize effectively to real-world ecological environments.
[0004] To address the challenge of recognizing classes that were not present during training, researchers have explored zero-shot learning (ZSL) techniques. Early ZSL architectures attempt to classify previously unseen categories by leveraging semantic relationships between visual data and auxiliary information such as textual descriptions or attribute annotations. In such approaches, visual features extracted from images are mapped to a semantic embedding space that represents conceptual information about the species. By linking visual features to semantic attributes, the system can infer the identity of unseen species even without direct training examples. While this paradigm has demonstrated promising results in certain controlled settings, many existing ZSL models primarily focus on establishing a visual-textual association and do not sufficiently consider other contextual factors that influence species appearance and behavior.
[0005] In natural ecosystems, the observable characteristics of animals are influenced by numerous environmental conditions including habitat type, weather patterns, seasonal cycles, and ecological interactions. These contextual factors can significantly alter the visual traits, activity patterns, and behavioral attributes of species. However, most conventional zero-shot learning frameworks rely solely on static semantic descriptions or attribute vectors, which may not adequately capture the dynamic ecological conditions under which wildlife observations occur. As a result, the recognition performance of such models tends to degrade when applied to complex and heterogeneous environments where visual signals alone are insufficient for reliable identification.
[0006] Thus, in light of the above-stated discussion, there exists a need for a computer-implemented unseen wildlife species identification system using an Adaptive Semantic Attribute Fusion Network (ASAF-Net).
SUMMARY OF THE DISCLOSURE
[0007] The following is a summary description of illustrative embodiments of the invention. It is provided as a preface to assist those skilled in the art to more rapidly assimilate the detailed design discussion which ensues and is not intended in any way to limit the scope of the claims which are appended hereto in order to particularly point out the invention.
[0008] According to illustrative embodiments, the present disclosure focuses on a computer-implemented unseen wildlife species identification system using an Adaptive Semantic Attribute Fusion Network (ASAF-Net) which overcomes the above-mentioned disadvantages or provide the users with a useful or commercial choice.
[0009] An objective of the present disclosure is to design a multi-modal input framework that integrates camera images or videos, semantic attribute descriptions, ontology data, environmental metadata, and optional behavioral observations for comprehensive wildlife recognition.
[0010] Another objective of the present disclosure is to develop an Adaptive Semantic Attribute Fusion Network (ASAF-Net) capable of identifying unseen wildlife species by leveraging multi-modal data sources including visual, semantic, and environmental inputs.
[0011] Another objective of the present disclosure is to implement neural feature encoding mechanisms that transform heterogeneous data modalities into a unified latent representation space for efficient cross-modal learning and comparison.
[0012] Another objective of the present disclosure is to establish a context-aware adaptive fusion mechanism that dynamically adjusts the contribution of each data modality based on environmental conditions such as time of day, seasonal variations, habitat characteristics, and weather conditions.
[0013] Another objective of the present disclosure is to enable cross-domain semantic alignment between known and unseen species representations within a shared semantic embedding space to facilitate zero-shot species classification.
[0014] Another objective of the present disclosure is to develop a self-calibration mechanism capable of updating internal model parameters using high-confidence predictions and environmental data streams without requiring complete retraining of the system.
[0015] Another objective of the present disclosure is to enhance species prediction accuracy and robustness in dynamic ecological environments through adaptive fusion of visual, semantic, and environmental features.
[0016] Another objective of the present disclosure is to support real-time wildlife monitoring by enabling compatibility with IoT-enabled devices such as camera traps, environmental sensors, and edge computing systems.
[0017] Another objective of the present disclosure is to reduce dependency on large manually labeled datasets by enabling recognition of previously unseen species using semantic prototypes and attribute-based learning.
[0018] Yet another objective of the present disclosure is to provide a scalable intelligent biodiversity monitoring framework that can be deployed across large wildlife conservation networks for continuous ecological observation and automated species identification.
[0019] In light of the above, a computer-implemented unseen wildlife species identification system, the system comprises a computing unit comprising a processor and a non-transitory memory storing machine-readable instructions executable by the processor. The system also includes an image input module configured to receive wildlife images captured from camera-trap devices, drones, or image acquisition sensors. The system also includes a pre-processing module configured to perform pre-processing including resizing, normalization, and enhancement to generate visual input data. The system also includes a semantic attribute module configured to receive textual descriptions, attribute vectors, or ontology-based representations associated with animal species and to encode the textual descriptions into semantic feature embeddings. The system also includes an environmental context module configured to collect contextual metadata associated with an observation event and to encode the contextual metadata into environmental feature vectors. The system also includes a modality-specific encoder configured to transform the visual input data, semantic feature embeddings, and environmental feature vectors into corresponding latent representations within a common embedding space. The system also includes an adaptive semantic attribute fusion module comprising a context-aware weighting network configured to dynamically assign modality weights to the latent representations based on contextual conditions. The system also includes a cross-domain alignment module configured to align representations of known species and candidate unseen species within the common embedding space using prototype representations derived from semantic attributes and learned feature distributions. The system also includes an output module configured to determine a predicted species identity for an observed animal instance by computing similarity between the fused embedding and species prototypes corresponding to known and unseen species.
[0020] In one embodiment, the modality-specific encoder comprises a visual encoder implemented using a convolutional neural network architecture and semantic and environmental encoders implemented using multilayer perceptrons or transformer-based networks.
[0021] In one embodiment, the adaptive semantic attribute fusion module further comprises projection heads configured to transform visual, semantic, and environmental representations into modality-specific embeddings prior to fusion.
[0022] In one embodiment, the cross-domain alignment module further comprises a domain discrepancy minimization mechanism configured to reduce distribution differences between visual and semantic feature domains.
[0023] In one embodiment, the image input module is further configured to receive image data from a plurality of distributed wildlife monitoring devices including camera traps, unmanned aerial vehicles, and stationary environmental monitoring sensors deployed in natural habitats.
[0024] In one embodiment, the pre-processing module is further configured to perform image enhancement operations comprising noise reduction, contrast adjustment, deblurring, and data normalization in order to improve feature extraction accuracy.
[0025] In one embodiment, the semantic attribute module is configured to generate semantic embeddings using a pre-trained language model, attribute embedding network, or ontology-based encoding framework that converts species descriptions into fixed-length semantic vectors.
[0026] In one embodiment, the semantic attribute module is further configured to process species attribute information comprising at least one of body color, body size, texture pattern, anatomical structure, behavioral characteristics, or ecological habitat information.
[0027] In one embodiment, the environmental context module is configured to collect contextual metadata comprising at least one of geographic location coordinates, elevation, temperature, humidity, time of day, season, habitat classification, and weather conditions.
[0028] In one embodiment, the environmental context module is further configured to encode discrete contextual variables using embedding layers and continuous contextual variables using normalization functions to produce environmental feature vectors.
[0029] These and other advantages will be apparent from the present application of the embodiments described herein.
[0030] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
[0031] These elements, together with the other aspects of the present disclosure and various features are pointed out with particularity in the claims annexed hereto and form a part of the present disclosure. For a better understanding of the present disclosure, its operating advantages, and the specified object attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated exemplary embodiments of the present disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0032] To describe the technical solutions in the embodiments of the present disclosure or in the prior art more clearly, the following briefly describes the accompanying drawings required for describing the embodiments or the prior art. Apparently, the accompanying drawings in the following description merely show some embodiments of the present disclosure, and a person of ordinary skill in the art can derive other implementations from these accompanying drawings without creative efforts. All of the embodiments or the implementations shall fall within the protection scope of the present disclosure.
[0033] The advantages and features of the present disclosure will become better understood with reference to the following detailed description taken in conjunction with the accompanying drawing, in which:
[0034] FIG. 1 illustrates a flowchart outlining sequential step involved in a computer-implemented unseen wildlife species identification system using an Adaptive Semantic Attribute Fusion Network (ASAF-Net), in accordance with an exemplary embodiment of the present disclosure;
[0035] FIG. 2A to FIG. 2C illustrates an ASAF-net architecture, a context based weightage of attributes and an inference and cross domain alignment algorithm, in accordance with an exemplary embodiment of the present disclosure.
[0036] Like reference, numerals refer to like parts throughout the description of several views of the drawing;
[0037] The computer-implemented unseen wildlife species identification system using an Adaptive Semantic Attribute Fusion Network (ASAF-Net), which like reference letters indicate corresponding parts in the various figures. It should be noted that the accompanying figure is intended to present illustrations of exemplary embodiments of the present disclosure. This figure is not intended to limit the scope of the present disclosure. It should also be noted that the accompanying figure is not necessarily drawn to scale.
DETAILED DESCRIPTION OF THE DISCLOSURE
[0038] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure.
[0039] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It may be apparent to one skilled in the art that embodiments of the present disclosure may be practiced without some of these specific details.
[0040] Various terms as used herein are shown below. To the extent a term is used, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0041] The terms “a” and “an” herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced items.
[0042] The terms “having”, “comprising”, “including”, and variations thereof signify the presence of a component.
[0043] Referring now to FIG. 1 to FIG. 2 to describe various exemplary embodiments of the present disclosure. FIG. 1 illustrates a flowchart outlining sequential step involved in a computer-implemented unseen wildlife species identification system using an Adaptive Semantic Attribute Fusion Network (ASAF-Net), in accordance with an exemplary embodiment of the present disclosure.
[0044] A computer-implemented unseen wildlife species identification system 100, the system 100 comprises a computing unit 102 comprising a processor and a non-transitory memory storing machine-readable instructions executable by the processor.
[0045] The system also includes an image input module 104 configured to receive wildlife images captured from camera-trap devices, drones, or image acquisition sensors. The image input module 104 is further configured to receive image data from a plurality of distributed wildlife monitoring devices including camera traps, unmanned aerial vehicles, and stationary environmental monitoring sensors deployed in natural habitats.
[0046] The system also includes a pre-processing module 106 configured to perform pre-processing including resizing, normalization, and enhancement to generate visual input data. The pre-processing module 106 is further configured to perform image enhancement operations comprising noise reduction, contrast adjustment, deblurring, and data normalization in order to improve feature extraction accuracy.
[0047] The system also includes a semantic attribute module 108 configured to receive textual descriptions, attribute vectors, or ontology-based representations associated with animal species and to encode the textual descriptions into semantic feature embeddings. The semantic attribute module 108 is configured to generate semantic embeddings using a pre-trained language model, attribute embedding network, or ontology-based encoding framework that converts species descriptions into fixed-length semantic vectors. The semantic attribute module 108 is further configured to process species attribute information comprising at least one of body color, body size, texture pattern, anatomical structure, behavioral characteristics, or ecological habitat information.
[0048] The system also includes an environmental context module 110 configured to collect contextual metadata associated with an observation event and to encode the contextual metadata into environmental feature vectors. The environmental context module 110 is configured to collect contextual metadata comprising at least one of geographic location coordinates, elevation, temperature, humidity, time of day, season, habitat classification, and weather conditions. The environmental context module 110 is further configured to encode discrete contextual variables using embedding layers and continuous contextual variables using normalization functions to produce environmental feature vectors.
[0049] The system also includes a modality-specific encoder 112 configured to transform the visual input data, semantic feature embeddings, and environmental feature vectors into corresponding latent representations within a common embedding space. The modality-specific encoder 112 comprises a visual encoder implemented using a convolutional neural network architecture and semantic and environmental encoders implemented using multilayer perceptrons or transformer-based networks.
[0050] The system also includes an adaptive semantic attribute fusion module 114 comprising a context-aware weighting network configured to dynamically assign modality weights to the latent representations based on contextual conditions. The adaptive semantic attribute fusion module 114 further comprises projection heads configured to transform visual, semantic, and environmental representations into modality-specific embeddings prior to fusion.
[0051] The system also includes a cross-domain alignment module 116 configured to align representations of known species and candidate unseen species within the common embedding space using prototype representations derived from semantic attributes and learned feature distributions. The cross-domain alignment module 116 further comprises a domain discrepancy minimization mechanism configured to reduce distribution differences between visual and semantic feature domains.
[0052] The system also includes an output module 118 configured to determine a predicted species identity for an observed animal instance by computing similarity between the fused embedding and species prototypes corresponding to known and unseen species.
[0053] FIG. 1 illustrates a flowchart outlining sequential step involved in a computer-implemented unseen wildlife species identification system using an Adaptive Semantic Attribute Fusion Network (ASAF-Net).
[0054] At 102, the system lies a computing unit 102 comprising a processor and a non-transitory memory storing machine-readable instructions executable by the processor. The computing unit coordinates the execution of each module of the architecture, manages data flow among the components, and performs the computational operations required for feature extraction, attribute fusion, alignment, and inference.
[0055] At 104, the operational workflow begins with the image input module 104, which is responsible for acquiring wildlife imagery from various monitoring sources deployed in natural environments. These sources may include camera-trap devices placed in forests, drones used for aerial wildlife observation, or other automated image acquisition sensors installed in ecological monitoring networks. The image input module receives raw images or short image sequences capturing animals present within the monitored habitat. Since such images are often captured under uncontrolled environmental conditions, they may contain variations in lighting, motion blur, background clutter, or partial occlusions of animals. The module therefore ensures that these raw observations are properly transferred to the next stage of processing so that the system can begin analyzing the visual information contained within them.
[0056] At 106, the received images are forwarded to the pre-processing module 106, which prepares the visual data for feature extraction. This module performs several image conditioning operations designed to standardize and enhance the input data. The pre-processing operations include resizing the images to a consistent resolution compatible with the neural network architecture, normalizing pixel intensities to maintain uniform value ranges, and applying enhancement techniques that may include noise reduction, contrast adjustment, or sharpening. By transforming the raw images into standardized visual input data, the pre-processing module ensures that the subsequent neural encoders receive high-quality inputs that allow more accurate extraction of visual features.
[0057] At 108, the system incorporates semantic knowledge through the semantic attribute module 108. This module receives descriptive information about animal species derived from textual sources such as biological field guides, wildlife ontologies, ecological databases, or expert-defined attribute lists. The semantic information may include descriptions of species characteristics such as body color, size, distinctive markings, fur patterns, horn structure, tail length, or behavioral traits. The semantic attribute module converts these textual descriptions or attribute vectors into numerical semantic feature embeddings using embedding models or neural encoding techniques. These semantic embeddings represent conceptual knowledge about species and allow the system to understand relationships between different species based on their descriptive characteristics. Importantly, this semantic representation enables the system to recognize species that were not visually present during the training stage because the semantic descriptions can still be encoded and compared with visual observations.
[0058] At 110, the system integrates contextual ecological information through the environmental context module 110. This module collects metadata associated with the observation event during which the wildlife image was captured. Such contextual information may include geographic location in the form of GPS coordinates, elevation, habitat classification, temperature, humidity, time of day, seasonal indicators, or other environmental sensor readings. Many animal species exhibit strong correlations with specific environmental conditions, such as nocturnal activity patterns, seasonal migration behaviors, or habitat-specific distributions. The environmental context module processes this metadata and converts it into structured environmental feature vectors using normalization and encoding techniques. These vectors capture the ecological context surrounding the observation and provide additional cues that can assist in narrowing down the list of candidate species.
[0059] At 112, and environmental feature vectors are prepared, they are supplied to the modality-specific encoder 112. The purpose of this encoder is to transform the heterogeneous input modalities into a unified representation that can be analyzed jointly by the system. Each modality is processed through a dedicated encoder architecture suited to its data type. For instance, visual data may be processed through convolutional neural networks, while semantic and environmental inputs may be encoded using multilayer perceptrons or transformer-based encoders. These encoders map the different modalities into latent representations within a common embedding space, ensuring that information from visual observations, semantic descriptions, and environmental context can be directly compared and combined. By projecting the modalities into the same feature space, the system establishes a common framework for multi-modal reasoning.
[0060] At 114, the encoded representations are then transmitted to the adaptive semantic attribute fusion module 114, which acts as the central intelligence of the system. This module integrates the latent representations derived from the different modalities and produces a unified fused embedding that represents the observed animal instance. The module incorporates a context-aware weighting network that dynamically determines the importance of each modality based on the environmental conditions associated with the observation. For example, if an image is captured at night and visual clarity is reduced, the weighting network may increase the influence of environmental context and semantic attributes. Conversely, when the image quality is high and visual features are clearly visible, the visual modality may receive greater weighting. The adaptive weighting mechanism multiplies each modality’s latent representation by a corresponding weight factor before combining them to form a single fused embedding. This dynamic fusion process enables the system to remain robust across varying environmental conditions and sensor qualities.
[0061] At 116, the system proceeds to the cross-domain alignment module 116, which ensures that the feature representations of observed species and candidate unseen species are properly aligned within the common embedding space. In wildlife monitoring scenarios, the system may encounter species that were not included in the training dataset. The cross-domain alignment module addresses this challenge by constructing prototype representations for both known and unseen species using semantic attributes and previously learned feature distributions. The module aligns the visual-semantic fused embeddings with these species prototypes by minimizing discrepancies between the domains of visual observations and semantic descriptions. Through this alignment process, the system learns transferable relationships that enable it to generalize knowledge from known species to unseen species sharing similar attributes or ecological characteristics.
[0062] At 118, the aligned fused representation is provided to the output module 118, which performs the final inference step. This module determines the predicted species identity of the observed animal instance by computing similarity scores between the fused embedding and the stored species prototypes representing both known and unseen classes. Similarity measures such as cosine similarity or Euclidean distance may be used to identify the species prototype that most closely matches the observed embedding. The output module then selects the species corresponding to the highest similarity score as the predicted classification result. In addition to the predicted species label, the module may also generate a confidence score indicating the reliability of the prediction.
[0063] FIG. 2A illustrates an ASAF-net architecture. At the left side of the architecture, the system begins with the visual feature extraction unit, which is responsible for processing wildlife images captured by camera traps, drones, or other imaging sensors deployed in natural habitats. This unit typically employs convolutional neural network backbones or other deep visual encoders to analyze the input images and extract meaningful visual features such as body shape, fur patterns, color distribution, texture, and structural characteristics of animals appearing in the images. The visual feature extraction unit converts raw image pixels into high-level feature vectors that represent the observable characteristics of the detected animal. These representations serve as the primary visual evidence for species identification and are forwarded to the central fusion module.
[0064] Parallel to the visual processing branch, the system includes a semantic attribute encoder, which processes textual or attribute-based descriptions associated with animal species. These semantic inputs may originate from wildlife knowledge bases, biological ontologies, field guides, or expert-defined attribute vectors describing features such as body size, fur color, horn shape, tail length, or distinctive markings. The semantic attribute encoder converts these textual descriptions or attribute lists into numerical embedding vectors using language models, embedding networks, or multilayer perceptrons. Through this encoding process, semantic knowledge about species is transformed into a machine-readable representation that can be compared with visual features. This semantic branch enables the system to perform zero-shot recognition because it allows the model to understand the characteristics of species that have not been visually observed during training.
[0065] A third input pathway is represented by the environmental context encoder, which introduces contextual information describing the conditions under which the wildlife observation occurs. This module processes environmental metadata such as geographic location (GPS coordinates), elevation, habitat type, temperature, humidity, season, and time of day. These environmental attributes provide valuable ecological cues that influence animal behavior and species distribution. For example, certain animals may only appear in specific regions, during night time hours, or under particular seasonal conditions. The environmental context encoder converts these metadata parameters into structured feature vectors through normalization, embedding layers, or lightweight neural networks. By representing environmental factors numerically, the system can incorporate ecological knowledge into the decision-making process.
[0066] The outputs from the visual feature extraction unit, the semantic attribute encoder, and the environmental context encoder converge into the adaptive semantic attribute fusion module, which forms the core component of the ASAF-Net architecture. This module integrates the heterogeneous feature representations produced by the three input branches. Unlike conventional fusion mechanisms that treat all data sources equally, the adaptive fusion module dynamically adjusts the importance of each modality according to the context of the observation. A context-aware weighting mechanism evaluates the relevance of visual, semantic, and environmental features and assigns adaptive weights to each modality. For instance, in low-light night time conditions where visual quality may be degraded, the system may assign greater importance to environmental cues and semantic attributes. Conversely, under clear daylight conditions, visual features may receive higher weighting. The fusion module combines the weighted feature vectors into a unified latent representation that captures the most informative characteristics across all modalities.
[0067] The fused embedding generated by the adaptive fusion module is then transmitted to the classification unit, which performs species prediction. The classification unit compares the fused feature representation with stored prototypes or embeddings representing known and candidate species classes. Through similarity calculations such as cosine similarity or Euclidean distance, the classifier identifies the species whose prototype most closely matches the fused representation. Because the semantic attributes of unseen species can be encoded even without visual training samples, the classifier can infer the identity of species that were not present in the training dataset. This capability enables zero-shot or generalized zero-shot species recognition, which is critical for wildlife monitoring scenarios where rare or previously undocumented species may appear.
[0068] An additional component shown in the architecture is the self-calibration module, which is connected to the classification unit and the fusion module. The purpose of the self-calibration module is to allow the system to adapt continuously to changing environmental conditions and evolving data distributions without requiring full retraining of the deep learning model. The self-calibration mechanism monitors prediction confidence levels and incoming feature distributions during operation. When the system produces highly confident predictions, those predictions may be treated as pseudo-labels that help refine internal parameters, such as modality weights or class prototypes. The calibration module performs lightweight parameter adjustments using small neural layers or statistical updates, enabling the system to maintain accuracy even as environmental conditions change across seasons, habitats, or sensor configurations.
[0069] The feedback pathway illustrated in the figure indicates that calibration updates can influence both the classification process and the fusion module. By refining modality weights and prototype representations over time, the system gradually improves its recognition capability based on accumulated observational data. This feedback mechanism allows ASAF-Net to evolve as new environmental patterns emerge or as the system encounters species in new contexts.
[0070] FIG. 2B illustrates a context based weightage of attributes. At the left side of the diagram, a context-aware attribute weighting component is depicted as the primary controlling module responsible for analyzing the contextual conditions associated with an observation. This module interprets metadata describing the environmental state in which the animal image was captured. Such contextual information may include geographic location, time of day, season, habitat characteristics, weather conditions, temperature, humidity, or other sensor-derived telemetry. The purpose of the context-aware weighting unit is to evaluate how these contextual factors influence the reliability and importance of different data modalities. Since wildlife observations often occur under highly variable environmental conditions, the system must determine which sources of information are most trustworthy or informative for a given situation.
[0071] The diagram further illustrates three principal attribute streams visual, semantic, and environmental that serve as inputs to the adaptive weighting process. The visual input corresponds to features extracted from images captured by camera traps, drones, or surveillance devices deployed in wildlife habitats. These visual features may represent the shape, color, texture, markings, or body structure of the observed animal. While visual cues are typically the most direct indicators of species identity, their reliability may fluctuate depending on image quality, lighting conditions, motion blur, occlusions, or partial visibility of the animal. For instance, images captured at night or during poor weather conditions may contain limited visual detail, reducing the usefulness of purely visual analysis.
[0072] The semantic attribute input represents descriptive knowledge about species characteristics derived from biological descriptions, ecological databases, or expert-curated attribute lists. These semantic attributes may include properties such as fur color, body size, horn structure, tail shape, distinctive patterns, or behavioral tendencies. Semantic attributes serve as a bridge between known and unseen species, enabling the system to infer the identity of animals that were not part of the training dataset. Because these descriptions are independent of the actual image data, semantic attributes remain valuable even when visual features are degraded or incomplete. Consequently, the semantic modality provides an important source of generalizable knowledge for zero-shot recognition.
[0073] The third modality shown in the diagram is the environmental attribute input, which captures contextual ecological information surrounding the observation event. Environmental attributes may include geographical coordinates, elevation, vegetation type, temperature, humidity, time of observation, seasonal patterns, or habitat classification. Such contextual factors play a crucial role in wildlife identification because many species exhibit strong habitat preferences and behavioral patterns tied to environmental conditions. For example, certain animals may only appear in specific geographic regions, migrate during particular seasons, or remain active primarily during night time hours. Incorporating these environmental cues helps the system narrow down the list of possible species candidates.
[0074] These three attribute streams converge into the adaptive weights generation module, which determines the importance assigned to each modality during the classification process. The context-aware weighting mechanism evaluates the incoming contextual information and dynamically computes weight coefficients representing the contribution of each attribute source. These coefficients regulate how strongly visual, semantic, and environmental features influence the final fused representation used for classification. The adaptive weights allow the system to balance the modalities depending on the circumstances of the observation. For example, when clear daytime images are available, the weighting mechanism may assign higher importance to visual features. Conversely, when image quality is poor or lighting is insufficient, the model may rely more heavily on environmental context and semantic knowledge.
[0075] The diagram indicates that the adaptive weighting process produces a set of modality weight parameters, represented by the output block labeled with a weight variable such as w1. In practice, multiple weights may be generated for each modality or feature dimension, enabling fine-grained control over how the information sources are combined. These weight values are then used by the attribute fusion module of the ASAF-Net architecture to scale and integrate the feature vectors from different modalities. The weighted features are combined to form a unified representation that reflects the most relevant information for the current environmental context.
[0076] FIG. 2C illustrates an inference and cross domain alignment algorithm. At the left side of the diagram, the process begins with the visual features extracted from wildlife images captured by camera traps or monitoring devices. These visual features represent high-level representations of the animal present in the image and are typically generated through deep convolutional neural networks or other visual encoding models. The visual encoder processes the raw pixel data and transforms it into a compact feature vector that captures distinctive characteristics of the observed animal, such as body structure, texture, fur patterns, shape of limbs, and other morphological attributes. These visual features serve as the primary evidence obtained directly from the captured image and are subsequently projected into a semantic representation space where they can be compared with semantic descriptions of animal species.
[0077] Parallel to the visual pathway, the system incorporates semantic embeddings, which represent the textual or attribute-based descriptions of animal species. These semantic embeddings are generated from biological knowledge sources such as species attribute lists, wildlife databases, ecological ontologies, or descriptive text extracted from scientific literature. The semantic attributes may include characteristics like fur color, body size, horn type, tail length, habitat preference, or behavioral traits. Through embedding techniques such as word embeddings, attribute vector encoding, or language models, the semantic descriptions are transformed into numerical vectors that represent conceptual knowledge about species. These semantic embeddings serve as a form of prior knowledge that describes both known and unseen species.
[0078] Both the visual feature vectors and semantic embeddings converge into a shared semantic space, which functions as a common latent representation environment where information from both modalities can be aligned and compared. The semantic space acts as an intermediate mapping layer that ensures that visual patterns extracted from images correspond to semantic attributes describing species. By projecting visual features into this semantic space, the model learns relationships between visual characteristics and semantic descriptions during the training process. This alignment enables the system to generalize knowledge from known species to unseen species by leveraging the similarity between semantic attribute descriptions.
[0079] The semantic space is connected to a zero-shot classifier, which performs the final inference step for species identification. The zero-shot classifier operates by comparing the visual embedding of the observed animal with the semantic representations of candidate species classes. Instead of relying solely on labeled visual examples for each species, the classifier determines the species identity based on the similarity between the image-derived embedding and the semantic prototypes representing different species. If a previously unseen species shares semantic attributes with known species, the classifier can infer its identity by identifying the closest matching semantic representation within the shared space. This mechanism allows the model to recognize species that were never visually observed during training, which is a key capability required in wildlife monitoring where rare or newly discovered species may appear.
[0080] The diagram also includes a domain discrepancy loss component, which plays a crucial role in achieving cross-domain alignment between visual and semantic representations. In many machine learning scenarios, visual features and semantic embeddings originate from different data distributions, which can lead to misalignment in the shared representation space. The domain discrepancy loss measures the difference between the distributions of visual features and semantic representations and encourages the model to minimize this difference during training. By reducing the domain gap, the model ensures that visual features and semantic attributes occupy a consistent and aligned region of the embedding space. This alignment improves the reliability of similarity comparisons used by the zero-shot classifier.
[0081] The connections in the diagram show that the domain discrepancy loss receives inputs from both the visual feature stream and the semantic space, allowing it to monitor how well the visual embeddings align with their corresponding semantic descriptions. During training, optimization algorithms adjust the parameters of the visual encoders and semantic projection layers to minimize this discrepancy. As a result, the learned embedding space becomes structured such that semantically related species are located closer together while unrelated species remain separated. This structured embedding space enables effective transfer of knowledge from known species to unseen species.
[0082] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it will be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0083] A person of ordinary skill in the art may be aware that, in combination with the examples described in the embodiments disclosed in this specification, units and algorithm steps may be implemented by electronic hardware, computer software, or a combination thereof.
[0084] The foregoing descriptions of specific embodiments of the present disclosure have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed, and many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present disclosure and its practical application, and to thereby enable others skilled in the art to best utilize the present disclosure and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omissions and substitutions of equivalents are contemplated as circumstances may suggest or render expedient, but such omissions and substitutions are intended to cover the application or implementation without departing from the scope of the present disclosure.
[0085] Disjunctive language such as the phrase “at least one of X, Y, Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z). Thus, such disjunctive language is not generally intended to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.
[0086] In a case that no conflict occurs, the embodiments in the present disclosure and the features in the embodiments may be mutually combined. The foregoing descriptions are merely specific implementations of the present disclosure, but are not intended to limit the protection scope of the present disclosure. Any variation or replacement readily figured out by a person skilled in the art within the technical scope disclosed in the present disclosure shall fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the protection scope of the claims.
, Claims:I/We Claim:
1. A computer-implemented unseen wildlife species identification system (100), the system (100) comprising:
a computing unit (102) comprising a processor and a non-transitory memory storing machine-readable instructions executable by the processor;
an image input module (104) configured to receive wildlife images captured from camera-trap devices, drones, or image acquisition sensors:
a pre-processing module (106) configured to perform pre-processing including resizing, normalization, and enhancement to generate visual input data;
a semantic attribute module (108) configured to receive textual descriptions, attribute vectors, or ontology-based representations associated with animal species and to encode the textual descriptions into semantic feature embeddings;
an environmental context module (110) configured to collect contextual metadata associated with an observation event and to encode the contextual metadata into environmental feature vectors;
a modality-specific encoder (112) configured to transform the visual input data, semantic feature embeddings, and environmental feature vectors into corresponding latent representations within a common embedding space;
an adaptive semantic attribute fusion module (114) comprising a context-aware weighting network configured to dynamically assign modality weights to the latent representations based on contextual conditions;
a cross-domain alignment module (116) configured to align representations of known species and candidate unseen species within the common embedding space using prototype representations derived from semantic attributes and learned feature distributions; and
an output module (118) configured to determine a predicted species identity for an observed animal instance by computing similarity between the fused embedding and species prototypes corresponding to known and unseen species.
2. The system (100) as claimed in claim 1, wherein the modality-specific encoder (112) comprises a visual encoder implemented using a convolutional neural network architecture and semantic and environmental encoders implemented using multilayer perceptrons or transformer-based networks.
3. The system (100) as claimed in claim 1, wherein the adaptive semantic attribute fusion module (114) further comprises projection heads configured to transform visual, semantic, and environmental representations into modality-specific embeddings prior to fusion.
4. The system (100) as claimed in claim 1, wherein the cross-domain alignment module (116) further comprises a domain discrepancy minimization mechanism configured to reduce distribution differences between visual and semantic feature domains.
5. The system (100) as claimed in claim 1, wherein the image input module (104) is further configured to receive image data from a plurality of distributed wildlife monitoring devices including camera traps, unmanned aerial vehicles, and stationary environmental monitoring sensors deployed in natural habitats.
6. The system (100) as claimed in claim 1, wherein the pre-processing module (106) is further configured to perform image enhancement operations comprising noise reduction, contrast adjustment, deblurring, and data normalization in order to improve feature extraction accuracy.
7. The system (100) as claimed in claim 1, wherein the semantic attribute module (108) is configured to generate semantic embeddings using a pre-trained language model, attribute embedding network, or ontology-based encoding framework that converts species descriptions into fixed-length semantic vectors.
8. The system (100) as claimed in claim 1, wherein the semantic attribute module (108) is further configured to process species attribute information comprising at least one of body color, body size, texture pattern, anatomical structure, behavioral characteristics, or ecological habitat information.
9. The system (100) as claimed in claim 1, wherein the environmental context module (110) is configured to collect contextual metadata comprising at least one of geographic location coordinates, elevation, temperature, humidity, time of day, season, habitat classification, and weather conditions.
10. The system (100) as claimed in claim 1, wherein the environmental context module (110) is further configured to encode discrete contextual variables using embedding layers and continuous contextual variables using normalization functions to produce environmental feature vectors.