Fine-Grained Cross-Modal Semantic Consistency in Natural Conservation Image Data from a Multi-Task Perspective.

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Fine-grained species classification relies on deep learning and cross-modal contrastive learning.
• Challenges include aligning representations from diverse species images and ambiguous natural language descriptions.
• Existing methods can suffer from encoder fluctuations, leading to poor representation quality.

Purpose of the Study:

• To improve fine-grained cross-modal representation alignment for species classification.
• To address challenges posed by data diversity and natural language ambiguity in conservation image data.
• To enhance the quality and robustness of cross-modal feature representations.

Main Methods:

• Proposed a residual attention network to stabilize momentum updates in cross-modal encoders.
• Introduced multi-task momentum encoding to bridge cross-modal information and improve mutual information.
• Aligned ambiguous natural language with invariant image features to mitigate contextual ambiguity.

Main Results:

• Achieved up to an 8% improvement on standardized image classification and cross-modal retrieval tasks.
• Demonstrated superior performance compared to similar models on public datasets.
• Successfully performed cross-modal retrieval and generation tasks for 8142 species on a custom dataset.

Conclusions:

• The proposed multi-task perspective of cross-modal momentum encoders effectively enhances fine-grained representation alignment.
• The method improves cross-modal mutual information, representation quality, and feature distribution.
• Validated effectiveness for fine-grained cross-modal image-text tasks in conservation areas.