Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search

• URL: http://arxiv.org/abs/2208.14365v2
• Date: Fri, 14 Jul 2023 03:07:59 GMT
• Title: Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search
• Authors: Shuanglin Yan, Hao Tang, Liyan Zhang and Jinhui Tang
• Abstract summary: Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. Most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities. We propose an efficient joint Multi-level Alignment Network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels.
• Score: 61.24539128142504
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Text-based person search (TBPS) is a challenging task that aims to search pedestrian images with the same identity from an image gallery given a query text. In recent years, TBPS has made remarkable progress and state-of-the-art methods achieve superior performance by learning local fine-grained correspondence between images and texts. However, most existing methods rely on explicitly generated local parts to model fine-grained correspondence between modalities, which is unreliable due to the lack of contextual information or the potential introduction of noise. Moreover, existing methods seldom consider the information inequality problem between modalities caused by image-specific information. To address these limitations, we propose an efficient joint Multi-level Alignment Network (MANet) for TBPS, which can learn aligned image/text feature representations between modalities at multiple levels, and realize fast and effective person search. Specifically, we first design an image-specific information suppression module, which suppresses image background and environmental factors by relation-guided localization and channel attention filtration respectively. This module effectively alleviates the information inequality problem and realizes the alignment of information volume between images and texts. Secondly, we propose an implicit local alignment module to adaptively aggregate all pixel/word features of image/text to a set of modality-shared semantic topic centers and implicitly learn the local fine-grained correspondence between modalities without additional supervision and cross-modal interactions. And a global alignment is introduced as a supplement to the local perspective. The cooperation of global and local alignment modules enables better semantic alignment between modalities. Extensive experiments on multiple databases demonstrate the effectiveness and superiority of our MANet.

Related papers

• TextCoT: Zoom In for Enhanced Multimodal Text-Rich Image Understanding [91.30065932213758]
  Large Multimodal Models (LMMs) have sparked a surge in research aimed at harnessing their remarkable reasoning abilities. We propose TextCoT, a novel Chain-of-Thought framework for text-rich image understanding. Our method is free of extra training, offering immediate plug-and-play functionality.
  arXiv  Detail & Related papers  (2024-04-15T13:54:35Z)
• Language Guided Domain Generalized Medical Image Segmentation [68.93124785575739]
  Single source domain generalization holds promise for more reliable and consistent image segmentation across real-world clinical settings. We propose an approach that explicitly leverages textual information by incorporating a contrastive learning mechanism guided by the text encoder features. Our approach achieves favorable performance against existing methods in literature.
  arXiv  Detail & Related papers  (2024-04-01T17:48:15Z)
• Text-guided Image Restoration and Semantic Enhancement for Text-to-Image Person Retrieval [11.798006331912056]
  The goal of Text-to-Image Person Retrieval (TIPR) is to retrieve specific person images according to the given textual descriptions. We propose a novel TIPR framework to build fine-grained interactions and alignment between person images and the corresponding texts.
  arXiv  Detail & Related papers  (2023-07-18T08:23:46Z)
• Cross-Modal Implicit Relation Reasoning and Aligning for Text-to-Image Person Retrieval [29.884153827619915]
  We present IRRA: a cross-modal Implicit Relation Reasoning and Aligning framework. It learns relations between local visual-textual tokens and enhances global image-text matching. The proposed method achieves new state-of-the-art results on all three public datasets.
  arXiv  Detail & Related papers  (2023-03-22T12:11:59Z)
• HGAN: Hierarchical Graph Alignment Network for Image-Text Retrieval [13.061063817876336]
  We propose a novel Hierarchical Graph Alignment Network (HGAN) for image-text retrieval. First, to capture the comprehensive multimodal features, we construct the feature graphs for the image and text modality respectively. Then, a multi-granularity shared space is established with a designed Multi-granularity Feature Aggregation and Rearrangement (MFAR) module. Finally, the ultimate image and text features are further refined through three-level similarity functions to achieve the hierarchical alignment.
  arXiv  Detail & Related papers  (2022-12-16T05:08:52Z)
• Learning to Model Multimodal Semantic Alignment for Story Visualization [58.16484259508973]
  Story visualization aims to generate a sequence of images to narrate each sentence in a multi-sentence story. Current works face the problem of semantic misalignment because of their fixed architecture and diversity of input modalities. We explore the semantic alignment between text and image representations by learning to match their semantic levels in the GAN-based generative model.
  arXiv  Detail & Related papers  (2022-11-14T11:41:44Z)
• Fine-Grained Semantically Aligned Vision-Language Pre-Training [151.7372197904064]
  Large-scale vision-language pre-training has shown impressive advances in a wide range of downstream tasks. Existing methods mainly model the cross-modal alignment by the similarity of the global representations of images and texts. We introduce LO, a fine-grained semantically aLigned visiOn-langUage PrE-training framework, which learns fine-grained semantic alignment from the novel perspective of game-theoretic interactions.
  arXiv  Detail & Related papers  (2022-08-04T07:51:48Z)
• CRIS: CLIP-Driven Referring Image Segmentation [71.56466057776086]
  We propose an end-to-end CLIP-Driven Referring Image framework (CRIS) CRIS resorts to vision-language decoding and contrastive learning for achieving the text-to-pixel alignment. Our proposed framework significantly outperforms the state-of-the-art performance without any post-processing.
  arXiv  Detail & Related papers  (2021-11-30T07:29:08Z)
• Step-Wise Hierarchical Alignment Network for Image-Text Matching [29.07229472373576]
  We propose a step-wise hierarchical alignment network (SHAN) that decomposes image-text matching into multi-step cross-modal reasoning process. Specifically, we first achieve local-to-local alignment at fragment level, following by performing global-to-local and global-to-global alignment at context level sequentially.
  arXiv  Detail & Related papers  (2021-06-11T17:05:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.