Diverse Semantics-Guided Feature Alignment and Decoupling for Visible-Infrared Person Re-Identification (2505.00619v1)

Abstract: Visible-Infrared Person Re-Identification (VI-ReID) is a challenging task due to the large modality discrepancy between visible and infrared images, which complicates the alignment of their features into a suitable common space. Moreover, style noise, such as illumination and color contrast, reduces the identity discriminability and modality invariance of features. To address these challenges, we propose a novel Diverse Semantics-guided Feature Alignment and Decoupling (DSFAD) network to align identity-relevant features from different modalities into a textual embedding space and disentangle identity-irrelevant features within each modality. Specifically, we develop a Diverse Semantics-guided Feature Alignment (DSFA) module, which generates pedestrian descriptions with diverse sentence structures to guide the cross-modality alignment of visual features. Furthermore, to filter out style information, we propose a Semantic Margin-guided Feature Decoupling (SMFD) module, which decomposes visual features into pedestrian-related and style-related components, and then constrains the similarity between the former and the textual embeddings to be at least a margin higher than that between the latter and the textual embeddings. Additionally, to prevent the loss of pedestrian semantics during feature decoupling, we design a Semantic Consistency-guided Feature Restitution (SCFR) module, which further excavates useful information for identification from the style-related features and restores it back into the pedestrian-related features, and then constrains the similarity between the features after restitution and the textual embeddings to be consistent with that between the features before decoupling and the textual embeddings. Extensive experiments on three VI-ReID datasets demonstrate the superiority of our DSFAD.

Diverse Semantics-Guided Feature Alignment and Decoupling for Visible-Infrared Person Re-Identification

The research paper titled "Diverse Semantics-Guided Feature Alignment and Decoupling for Visible-Infrared Person Re-Identification" addresses the complex and challenging task of Visible-Infrared Person Re-Identification (VI-ReID). The primary challenge in VI-ReID is the substantial modality discrepancy between visible and infrared images, which complicates the alignment of their features into a common embedding space. Moreover, style noise, such as differences in illumination and color contrast, introduces further complexity, reducing the identity discriminability and modality invariance of extracted features.

The paper proposes a novel framework named Diverse Semantics-guided Feature Alignment and Decoupling (DSFAD) network, specifically designed to align identity-relevant features from visible and infrared modalities into a textual embedding space and disentangle identity-irrelevant features within each modality. The approach hinges on three major components: Diverse Semantics-guided Feature Alignment (DSFA), Semantic Margin-guided Feature Decoupling (SMFD), and Semantic Consistency-guided Feature Restitution (SCFR).

1. Diverse Semantics-Guided Feature Alignment (DSFA): This module addresses the alignment of visible and infrared features into a textual embedding space by generating pedestrian descriptions with diverse sentence structures, using tools like ChatGPT to create varied sentence templates. By randomly selecting templates for description generation with LLaVA, this approach aims to prevent the model from overfitting to rigid semantic patterns, which is a notable limitation in approaches using fixed sentence structures for description generation. This module enhances both the semantic richness of features and the efficiency of cross-modality visual feature alignment.
2. Semantic Margin-Guided Feature Decoupling (SMFD): The SMFD module facilitates the disentangling of pedestrian-irrelevant style information (like illumination) from identity-related features. To ensure effective separation, the semantic margin loss constrains the identity-related features extracted to maintain a higher similarity to textual embeddings than style-related features. This methodology allows significant enhancement in filtering out identity-irrelevant information, increasing the identity discriminability.
3. Semantic Consistency-Guided Feature Restitution (SCFR): IN processing can inadvertently remove identity-contributing semantic information. The SCFR module addresses this potential issue by restoring useful identity-related information extracted from style features back into pedestrian-related representations. By maintaining semantic consistency between features before and after restitution, the SCFR optimizes feature extraction without sacrificing valuable pedestrian semantics.

Extensive experiments conducted on three VI-ReID datasets validate the superiority of DSFAD over existing methods, establishing notable improvements in Rank-1 accuracy and mean Average Precision (mAP) scores across various evaluation settings. The results demonstrate the effectiveness of DSFAD in enhancing cross-modality identity matching and semantic alignment, while successfully filtering style noise.

The paper curtails the introduction of fixed language patterns and improves semantic sensing through diverse sentence structures, which substantially mitigates the modality gap inherent in VI-ReID tasks. Practically, the DSFAD network is efficient by being single-stage, end-to-end trainable, and requiring only identity-relevant feature extraction during actual deployment, proving its usability across real-world scenarios.

Implications and Speculation

The implications of the DSFAD network are significant for intelligent surveillance systems requiring robust person re-identification across varying lighting conditions. The integration of textual semantics for feature alignment may pave the way for further applications of LLMs in multimodal tasks beyond VI-ReID. Future developments in AI related to feature alignment could explore adaptive semantic generation strategies that dynamically tailor the description generation process for further improvement in model robustness and accuracy, perhaps by integrating autonomous decision-making capabilities into these systems. The implications extend to enhancing the ability of surveillance solutions to monitor and identify individuals under diverse and challenging environmental conditions effectively.

Overall, the DSFAD network demonstrates a pragmatic and progressive method to overcome the limitations prevalent in VI-ReID tasks, offering insights into leveraging semantics for improved model performance and alignment efficacy.