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HINT: Composed Image Retrieval with Dual-path Compositional Contextualized Network
Mingyu Zhang, Zixu Li, Zhiwei Chen, Zhiheng Fu, Xiaowei Zhu, Jiajia Nie, Yinwei Wei, Yupeng Hu
TL;DR
Composed Image Retrieval must match a target image to a reference image modified by text, yet existing methods underuse contextual information and struggle with implicit dependencies and weak matching distinctions. HINT introduces dual contextual encoding, relevance quantification, and consistency constraints, achieving optimal results across all metrics on two CIR benchmarks.
Problem
Existing CIR methods neglect contextual information for discriminating matching samples, leaving implicit dependencies and differential amplification insufficiently addressed.
Method
HINT extracts intra-modal and cross-modal context, quantifies contextual relevance, and applies dual-path consistency constraints to amplify similarity differences.
Results
HINT achieves optimal results across all metrics on two CIR benchmark datasets.
Takeaways & Limitations
Context-aware encoding and similarity-difference amplification improve CIR performance in complex scenarios.
Abstract
from arXiv · showhide
Composed Image Retrieval (CIR) is a challenging image retrieval paradigm. It aims to retrieve target images from large-scale image databases that are consistent with the modification semantics, based on a multimodal query composed of a reference image and modification text. Although existing methods have made significant progress in cross-modal alignment and feature fusion, a key flaw remains: the neglect of contextual information in discriminating matching samples. However, addressing this limitation is not an easy task due to two challenges: 1) implicit dependencies and 2) the lack of a differential amplification mechanism. To address these challenges, we propose a dual-patH composItional coNtextualized neTwork (HINT), which can perform contextualized encoding and amplify the similarity differences between matching and non-matching samples, thus improving the upper performance of CIR models in complex scenarios. Our HINT model achieves optimal performance on all metrics across two CIR benchmark datasets, demonstrating the superiority of our HINT model. Codes are available at https://github.com/zh-mingyu/HINT.
1 School of Software, Shandong University
The paper addresses Composed Image Retrieval through multimodal context learning and cross-modal retrieval.
- The paper studies Composed Image Retrieval.
- Its approach involves multimodal context learning.
- The work concerns linguistic-visual context and cross-modal retrieval.
1. INTRODUCTION
CIR retrieves target images using a reference image and modification text, but existing methods underuse contextual information when distinguishing matches. HINT addresses this through dual contextual modeling and similarity-difference amplification.
- CIR retrieves target images consistent with modification semantics from a reference image and modification text.Unlike traditional retrieval, the text typically provides a modification instruction rather than a full description.
- Existing methods neglect contextual structure, weakening discrimination between matching and non-matching samples.They often simplify intra-image dependencies and text constraints through shallow attention mechanisms.
- HINT addresses implicit dependencies and missing discrepancy amplification through contextual encoding and similarity-difference enhancement.Its framework includes Dual Context Extraction, Quantification of Contextual Relevance, and Dual-Path Consistency Constraints.
2. METHODOLOGY
HINT models visual, textual, and cross-modal context in composed image retrieval, then quantifies contextual relevance and jointly optimizes contextual and cosine similarities to separate matching from non-matching samples.
- Dual Context Extraction: HINT extracts visual intra-modal and cross-modal context to build contextualized multimodal representations for composed image retrieval.The model captures associations among image regions and fuses visual context with modification-text features.
- Visual Context Modeling: Position encodings and attention-based interaction model local structural information and implicit dependencies among image regions.Each reference-image query receives positional information before multi-head attention and feed-forward processing.
- Cross-modal Context Modeling: Text features provide ordered textual context, which CCM concatenates with visual context before contextual multimodal fusion.The combined representation is processed to obtain fine-grained cross-modal context features.
- Quantification of Contextual Relevance: HINT quantifies contextual relevance by comparing context-aware multimodal features with target-image features across progressively larger channel subsets.The resulting relevance scores are averaged and fused across levels to supplement cosine similarity.
- Dual-path Consistency Constraints: Dual-Path Consistency Constraints combine contextual contrastive loss with rank loss to increase similarity for matching pairs and decrease it for non-matching pairs.The overall objective weights the contextual term with λ alongside the ranking term.
3. EXPERIMENTS
HINT is evaluated on open-domain CIRR and fashion-domain FashionIQ, with results, ablations, and case studies showing strong retrieval performance and the value of contextual modeling.
- Experimental Setup: HINT is evaluated using R@k and related subset metrics on CIRR, and category-level R@10 and R@50 averages on FashionIQ.CIRR also reports an average of R@5 and Rsubset@1.
- Performance Comparison: HINT improves performance on both CIRR and FashionIQ, with a more pronounced gain on FashionIQ’s fine-grained and semantically ambiguous attribute compositions.The authors attribute this domain difference to FashionIQ’s frequent semantic ambiguity and conflicts.
- Ablation Study: Removing VCM, CCM, or DCE decreases performance, supporting the contribution of visual, cross-modal, and dual-context modeling.These ablations are designed to test visual internal context, cross-modal context, and the DCE module.
- Ablation Study: Simplifying multi-channel similarity or replacing association quantization with cosine similarity prevents the model from reaching its optimal performance.The authors connect this result to the role of context-aware similarity computation and association quantization.
- Ablation Study: Removing Lrank causes a significant decline, while removing Lcontext also deteriorates performance, highlighting the roles of contrastive learning and contextual optimization.The comparison evaluates the independent effects of the batch-based contrastive loss and context loss.
- Case Study: In case studies, HINT ranks the CIRR target first while SPRC ranks it second, and HINT retrieves the FashionIQ target where SPRC fails.The authors associate these outcomes with better handling of contextual information, semantic ambiguity, and fine-grained attribute combinations.
4. CONCLUSION
The paper addresses contextual information being neglected in CIR matching and proposes HINT to model context and amplify similarity differences. HINT achieves optimal results across all metrics on two CIR benchmark datasets.
- Conclusion: HINT targets the neglect of contextual information in distinguishing matching samples, including implicit dependencies and insufficient similarity-difference amplification.These are identified as two challenges underlying the limitation of existing CIR models.
- Conclusion: HINT performs context-aware encoding and amplifies similarity differences between matching and non-matching samples for complex CIR scenarios.The proposed network is designed to improve the upper performance of CIR models.
- Conclusion: HINT achieves optimal results across all metrics on two CIR benchmark datasets.The conclusion presents this as evidence of the model’s effectiveness across the evaluated benchmarks.