EntityCLIP: Entity-Centric Image-Text Matching via Multimodal Attentive Contrastive Learning

• URL: http://arxiv.org/abs/2410.17810v1
• Date: Wed, 23 Oct 2024 12:12:56 GMT
• Title: EntityCLIP: Entity-Centric Image-Text Matching via Multimodal Attentive Contrastive Learning
• Authors: Yaxiong Wang, Yaxiong Wang, Lianwei Wu, Lechao Cheng, Zhun Zhong, Meng Wang,
• Abstract summary: In this paper, we work towards the textbfEntity-centric textbfImage-textbfText textbfMatching (EITM) problem. The challenge of this task mainly lies in the larger semantic gap in entity association modeling. We devise a multimodal attentive contrastive learning framework to adapt EITM problem, developing a model named EntityCLIP.
• Score: 38.30565103892611
• License:
• Abstract: Recent advancements in image-text matching have been notable, yet prevailing models predominantly cater to broad queries and struggle with accommodating fine-grained query intention. In this paper, we work towards the \textbf{E}ntity-centric \textbf{I}mage-\textbf{T}ext \textbf{M}atching (EITM), a task that the text and image involve specific entity-related information. The challenge of this task mainly lies in the larger semantic gap in entity association modeling, comparing with the general image-text matching problem.To narrow the huge semantic gap between the entity-centric text and the images, we take the fundamental CLIP as the backbone and devise a multimodal attentive contrastive learning framework to tam CLIP to adapt EITM problem, developing a model named EntityCLIP. The key of our multimodal attentive contrastive learning is to generate interpretive explanation text using Large Language Models (LLMs) as the bridge clues. In specific, we proceed by extracting explanatory text from off-the-shelf LLMs. This explanation text, coupled with the image and text, is then input into our specially crafted Multimodal Attentive Experts (MMAE) module, which effectively integrates explanation texts to narrow the gap of the entity-related text and image in a shared semantic space. Building on the enriched features derived from MMAE, we further design an effective Gated Integrative Image-text Matching (GI-ITM) strategy. The GI-ITM employs an adaptive gating mechanism to aggregate MMAE's features, subsequently applying image-text matching constraints to steer the alignment between the text and the image. Extensive experiments are conducted on three social media news benchmarks including N24News, VisualNews, and GoodNews, the results shows that our method surpasses the competition methods with a clear margin.

Related papers

• ComAlign: Compositional Alignment in Vision-Language Models [2.3250871476216814]
  We introduce Compositional Alignment (ComAlign) to discover more exact correspondence of text and image components. Our methodology emphasizes that the compositional structure extracted from the text modality must also be retained in the image modality. We train a lightweight network lying on top of existing visual and language encoders using a small dataset.
  arXiv  Detail & Related papers  (2024-09-12T16:46:41Z)
• Unified Text-to-Image Generation and Retrieval [96.72318842152148]
  We propose a unified framework in the context of Multimodal Large Language Models (MLLMs) We first explore the intrinsic discrimi abilities of MLLMs and introduce a generative retrieval method to perform retrieval in a training-free manner. We then unify generation and retrieval in an autoregressive generation way and propose an autonomous decision module to choose the best-matched one between generated and retrieved images.
  arXiv  Detail & Related papers  (2024-06-09T15:00:28Z)
• Image-Text Co-Decomposition for Text-Supervised Semantic Segmentation [28.24883865053459]
  This paper aims to learn a model capable of segmenting arbitrary visual concepts within images by using only image-text pairs without dense annotations. Existing methods have demonstrated that contrastive learning on image-text pairs effectively aligns visual segments with the meanings of texts. A text often consists of multiple semantic concepts, whereas semantic segmentation strives to create semantically homogeneous segments.
  arXiv  Detail & Related papers  (2024-04-05T17:25:17Z)
• 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)
• Multi-Modal Representation Learning with Text-Driven Soft Masks [48.19806080407593]
  We propose a visual-linguistic representation learning approach within a self-supervised learning framework. We generate diverse features for the image-text matching (ITM) task via soft-masking the regions in an image. We identify the relevant regions to each word by computing the word-conditional visual attention using multi-modal encoder.
  arXiv  Detail & Related papers  (2023-04-03T05:07:49Z)
• Scene Graph Based Fusion Network For Image-Text Retrieval [2.962083552798791]
  A critical challenge to image-text retrieval is how to learn accurate correspondences between images and texts. We propose a Scene Graph based Fusion Network (dubbed SGFN) which enhances the images'/texts' features through intra- and cross-modal fusion. Our SGFN performs better than quite a few SOTA image-text retrieval methods.
  arXiv  Detail & Related papers  (2023-03-20T13:22:56Z)
• 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)
• Image-Specific Information Suppression and Implicit Local Alignment for Text-based Person Search [61.24539128142504]
  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.
  arXiv  Detail & Related papers  (2022-08-30T16:14:18Z)
• 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)
• Referring Image Segmentation via Cross-Modal Progressive Comprehension [94.70482302324704]
  Referring image segmentation aims at segmenting the foreground masks of the entities that can well match the description given in the natural language expression. Previous approaches tackle this problem using implicit feature interaction and fusion between visual and linguistic modalities. We propose a Cross-Modal Progressive (CMPC) module and a Text-Guided Feature Exchange (TGFE) module to effectively address the challenging task.
  arXiv  Detail & Related papers  (2020-10-01T16:02:30Z)

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.