Visible-Infrared Person Re-Identification with Coarse Descriptions

Core Concepts

The author presents Refer-VI-ReID settings to address the challenge of matching visible images from infrared samples using coarse descriptions. The proposed YYDS structure decomposes and aggregates texture and color features, achieving remarkable improvements over existing methods.

Abstract

Visible-infrared person re-identification (VI-ReID) poses challenges due to modality discrepancies. The Refer-VI-ReID settings aim to match visible images from infrared samples using coarse descriptions. The YYDS structure decomposes and aggregates features, achieving significant performance gains on datasets. Existing VI-ReID methods focus on shared feature learning or feature compensation. Text-image person re-identification (TI-ReID) success inspires the proposed Refer-VI-ReID settings. The Y-Y-shape decomposition structure (YYDS) disentangles texture and color features for improved retrieval accuracy. The joint relation module in YYDS dynamically aggregates color and texture embeddings for complete representations. The CMKR algorithm addresses neighbor modality bias in k-reciprocal re-ranking for enhanced performance. Experimental results demonstrate the effectiveness of the proposed methods on various datasets.

Stats

Visible images: 22,258 Infrared images: 11,909 Training epochs: 80 Rank1 improvement with YYDS+CMKR: 10%

Quotes

"YYDS achieves Rank1 85.54% and mAP 81.64% in the all-search mode." "CMKR further improves the performance to Rank1 95.51% and mAP 93.77%."

Key Insights Distilled From

YYDS

by Yunhao Du,Zh... at arxiv.org 03-08-2024

https://arxiv.org/pdf/2403.04183.pdf

Deeper Inquiries

How can the Refer-VI-ReID settings be applied to other cross-modal tasks

The Refer-VI-ReID settings can be applied to other cross-modal tasks by leveraging coarse descriptions to complement missing information in different modalities. For instance, in the context of text-image retrieval, similar settings could be used to match textual descriptions with image samples. By incorporating language cues into the matching process, the model can benefit from additional semantic information that may not be present in one modality alone. This approach can enhance the robustness and accuracy of cross-modal retrieval tasks across various domains such as multimedia search, medical imaging analysis, or autonomous driving systems.

What are potential limitations of the YYDS structure in handling complex scenarios

While YYDS offers a novel approach for decomposing and aggregating texture and color features in person re-identification tasks, there are potential limitations when handling complex scenarios. One limitation is related to scalability and adaptability to diverse datasets with varying levels of complexity. The structure may struggle with highly intricate scenarios where multiple factors influence identification beyond just texture and color cues. Additionally, YYDS may face challenges in cases where there is significant variability within classes or when dealing with noisy or incomplete data that do not conform well to predefined decomposition structures.

How might advancements in natural language processing impact future developments in VI-ReID

Advancements in natural language processing (NLP) have the potential to significantly impact future developments in Visible-Infrared Person Re-Identification (VI-ReID). Improved NLP models can enhance the quality and relevance of textual descriptions provided for matching visible images with infrared samples. Advanced techniques like transformer-based models could enable more accurate extraction of semantic information from descriptions, leading to better alignment between query modalities and gallery modalities. Additionally, progress in multimodal learning approaches that combine vision and language understanding could further optimize VI-ReID systems by enabling more effective fusion of visual features with linguistic context for enhanced person recognition capabilities.