FAR-Net: Multi-Stage Fusion Network with Enhanced Semantic Alignment and Adaptive Reconciliation for Composed Image Retrieval

• URL: http://arxiv.org/abs/2507.12823v1
• Date: Thu, 17 Jul 2025 06:30:41 GMT
• Title: FAR-Net: Multi-Stage Fusion Network with Enhanced Semantic Alignment and Adaptive Reconciliation for Composed Image Retrieval
• Authors: Jeong-Woo Park, Young-Eun Kim, Seong-Whan Lee,
• Abstract summary: We propose FAR-Net, a multi-stage fusion framework designed with enhanced semantic alignment and adaptive reconciliation.<n>Experiments on CIRR and FashionIQ show consistent performance gains, improving Recall@1 by up to 2.4% and Recall@50 by 1.04% over existing methods.
• Score: 36.03123811283016
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Composed image retrieval (CIR) is a vision language task that retrieves a target image using a reference image and modification text, enabling intuitive specification of desired changes. While effectively fusing visual and textual modalities is crucial, existing methods typically adopt either early or late fusion. Early fusion tends to excessively focus on explicitly mentioned textual details and neglect visual context, whereas late fusion struggles to capture fine-grained semantic alignments between image regions and textual tokens. To address these issues, we propose FAR-Net, a multi-stage fusion framework designed with enhanced semantic alignment and adaptive reconciliation, integrating two complementary modules. The enhanced semantic alignment module (ESAM) employs late fusion with cross-attention to capture fine-grained semantic relationships, while the adaptive reconciliation module (ARM) applies early fusion with uncertainty embeddings to enhance robustness and adaptability. Experiments on CIRR and FashionIQ show consistent performance gains, improving Recall@1 by up to 2.4% and Recall@50 by 1.04% over existing state-of-the-art methods, empirically demonstrating that FAR Net provides a robust and scalable solution to CIR tasks.

Related papers

• OFFSET: Segmentation-based Focus Shift Revision for Composed Image Retrieval [59.377821673653436]
  Composed Image Retrieval (CIR) is capable of expressing users' intricate retrieval requirements flexibly.<n>CIR remains in its nascent stages due to two limitations: 1) inhomogeneity between dominant and noisy portions in visual data is ignored, leading to query feature degradation.<n>This work presents a focus mapping-based feature extractor, which consists of two modules: dominant portion segmentation and dual focus mapping.
  arXiv  Detail & Related papers  (2025-07-08T03:27:46Z)
• Representation Discrepancy Bridging Method for Remote Sensing Image-Text Retrieval [15.503629941274621]
  This study proposes a Representation Discrepancy Bridging (RDB) method for the Remote Image-Text Retrieval (RSITR) task.<n>Experiments on RSICD and RSITMD datasets show that the proposed RDB method achieves a 6%-11% improvement in mR metrics.
  arXiv  Detail & Related papers  (2025-05-22T14:59:30Z)
• TMCIR: Token Merge Benefits Composed Image Retrieval [13.457620649082504]
  Composed Image Retrieval (CIR) retrieves target images using a multi-modal query that combines a reference image with text describing desired modifications.<n>Current cross-modal feature fusion approaches for CIR exhibit an inherent bias in intention interpretation.<n>We propose a novel framework that advances composed image retrieval through two key innovations.
  arXiv  Detail & Related papers  (2025-04-15T09:14:04Z)
• OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning [19.22887628187884]
  A novel LVM-guided fusion framework with Object-aware and Contextual COntrastive learning is proposed.<n>A novel feature interaction fusion network is also designed to resolve information conflicts in fusion images caused by modality differences.<n>The effectiveness of the proposed method is validated, and exceptional performance is also demonstrated on downstream visual task.
  arXiv  Detail & Related papers  (2025-03-24T12:57:23Z)
• Cross-Modal Bidirectional Interaction Model for Referring Remote Sensing Image Segmentation [50.433911327489554]
  The goal of referring remote sensing image segmentation (RRSIS) is to generate a pixel-level mask of the target object identified by the referring expression.<n>To address the aforementioned challenges, a novel RRSIS framework is proposed, termed the cross-modal bidirectional interaction model (CroBIM)<n>To further forster the research of RRSIS, we also construct RISBench, a new large-scale benchmark dataset comprising 52,472 image-language-label triplets.
  arXiv  Detail & Related papers  (2024-10-11T08:28:04Z)
• From Text to Pixels: A Context-Aware Semantic Synergy Solution for Infrared and Visible Image Fusion [66.33467192279514]
  We introduce a text-guided multi-modality image fusion method that leverages the high-level semantics from textual descriptions to integrate semantics from infrared and visible images. Our method not only produces visually superior fusion results but also achieves a higher detection mAP over existing methods, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2023-12-31T08:13:47Z)
• RealignDiff: Boosting Text-to-Image Diffusion Model with Coarse-to-fine Semantic Re-alignment [112.45442468794658]
  We propose a two-stage coarse-to-fine semantic re-alignment method, named RealignDiff. In the coarse semantic re-alignment phase, a novel caption reward is proposed to evaluate the semantic discrepancy between the generated image caption and the given text prompt. The fine semantic re-alignment stage employs a local dense caption generation module and a re-weighting attention modulation module to refine the previously generated images from a local semantic view.
  arXiv  Detail & Related papers  (2023-05-31T06:59:21Z)
• Plug-and-Play Regulators for Image-Text Matching [76.28522712930668]
  Exploiting fine-grained correspondence and visual-semantic alignments has shown great potential in image-text matching. We develop two simple but quite effective regulators which efficiently encode the message output to automatically contextualize and aggregate cross-modal representations. Experiments on MSCOCO and Flickr30K datasets validate that they can bring an impressive and consistent R@1 gain on multiple models.
  arXiv  Detail & Related papers  (2023-03-23T15:42:05Z)

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.