Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification (2107.12666v2)

Abstract: Text-to-image person re-identification (ReID) aims to search for images containing a person of interest using textual descriptions. However, due to the significant modality gap and the large intra-class variance in textual descriptions, text-to-image ReID remains a challenging problem. Accordingly, in this paper, we propose a Semantically Self-Aligned Network (SSAN) to handle the above problems. First, we propose a novel method that automatically extracts semantically aligned part-level features from the two modalities. Second, we design a multi-view non-local network that captures the relationships between body parts, thereby establishing better correspondences between body parts and noun phrases. Third, we introduce a Compound Ranking (CR) loss that makes use of textual descriptions for other images of the same identity to provide extra supervision, thereby effectively reducing the intra-class variance in textual features. Finally, to expedite future research in text-to-image ReID, we build a new database named ICFG-PEDES. Extensive experiments demonstrate that SSAN outperforms state-of-the-art approaches by significant margins. Both the new ICFG-PEDES database and the SSAN code are available at https://github.com/zifyloo/SSAN.

• The paper introduces SSAN, which employs a Word Attention Module to align textual words with corresponding image parts without external parsing.
• It features a Multi-View Non-Local Network to capture inter-part relationships and a Compound Ranking loss to manage intra-class variance.
• Experimental results on ICFG-PEDES and CUHK-PEDES demonstrate SSAN's state-of-the-art performance and cross-domain robustness.

Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification

The paper "Semantically Self-Aligned Network for Text-to-Image Part-aware Person Re-identification" presents a comprehensive paper on addressing the challenges associated with text-to-image person re-identification (ReID). This task involves searching for images of a target individual based on textual descriptions, a critical capability in scenarios like surveillance where images may not be available. The authors introduce a novel framework, called the Semantically Self-Aligned Network (SSAN), designed to tackle the problems arising from modality gaps and significant intra-class variance in textual data.

Key Contributions and Methodology

1. Semantic Alignment via Word Attention: SSAN introduces a Word Attention Module (WAM) that aligns words from the textual description with specific visual parts of the image. This approach avoids dependency on external textual parsing tools, allowing for direct extraction of part-level features within the architecture. Unlike previous methods that rely heavily on external tools or computationally intensive cross-modal operations, WAM leverages well-aligned body parts as supervision to predict word-part correspondences effectively.
2. Multi-View Non-Local Network (MV-NLN): The architecture incorporates a multi-view non-local network to capture inter-part relationships within the image. MV-NLN facilitates the extraction of consistent semantic features aligned with complex noun phrases by considering the spatial relationships between body parts, such as those described by phrases like "holding a bag."
3. Compound Ranking Loss: To address the large intra-class variance found in textual descriptions, the authors propose a Compound Ranking (CR) loss. This loss incorporates both exact matching descriptions and descriptions of other images bearing the same identity, improving model robustness by effectively using varied textual supervision. The adaptive nature of the margin within the loss function accounts for the varying descriptive power of different descriptions.
4. ICFG-PEDES Database: To improve the evaluation of text-to-image ReID methodologies, the authors create a new database that features more complex image backgrounds and detailed, identity-centric textual descriptions compared to existing datasets like CUHK-PEDES. The increased complexity aims to simulate real-world scenarios more accurately.

Experimental Analysis

The authors conduct extensive experiments on both the new ICFG-PEDES and the established CUHK-PEDES databases. Results demonstrate that SSAN surpasses existing state-of-the-art methods by a considerable margin across multiple metrics, particularly in terms of Rank-1 accuracy. Additionally, the model showcases robustness in cross-domain settings, attributed to its capability to learn semantic alignment effectively without reliance on image-text pairs during inference.

Theoretical and Practical Implications

The introduction of SSAN significantly advances methodologies in the field of text-to-image ReID. The framework proposes solutions that effectively bridge the semantic gap between visual and textual data modalities within a unified network. The architecture's efficiency is further emphasized through its independence from external linguistic tools, facilitating broader applicability. Additionally, the introduction of a new, more challenging dataset positions the SSAN as a benchmark for developing future ReID models.

Future Prospects

The methodologies outlined in this paper pave the way for further explorations in multimodal alignment techniques, and the release of ICFG-PEDES encourages the development of more adaptive and comprehensive algorithms. The robustness of SSAN also suggests potential applications beyond surveillance, such as personalized content retrieval in multimedia archives. As advancements in natural language processing continue, integrating more sophisticated linguistic models could enhance textual feature extraction, reducing the intra-class variance more effectively.

In conclusion, this paper makes significant strides in text-to-image ReID by proposing a comprehensive framework that combines novel alignment strategies with a robust semantic understanding of both visual and textual domains. The implications of these advancements have the potential to extend far beyond the scope of ReID, contributing broadly to fields requiring intricate multimodal data integration.