# From Region to Patch: Attribute-Aware Foreground-Background Contrastive Learning for Fine-Grained Fashion Retrieval arXiv: https://arxiv.org/abs/2305.10260 ยท HTML: https://mlsanity.com/paper/2305.10260/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 46 references that are in the map. - First posted on arXiv 17 May 2023. ## Builds on (most cited first, 29 of 29) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020) - [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020) - [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018) - [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019) - [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020) - [Context-aware Biaffine Localizing Network for Temporal Sentence Grounding](https://mlsanity.com/paper/2103.11555/) (2021) - [Jointly Cross- and Self-Modal Graph Attention Network for Query-Based Moment Localization](https://mlsanity.com/paper/2008.01403/) (2020) - [Cross-domain Image Retrieval with a Dual Attribute-aware Ranking Network](https://mlsanity.com/paper/1505.07922/) (2015) - [Tree-Augmented Cross-Modal Encoding for Complex-Query Video Retrieval](https://mlsanity.com/paper/2007.02503/) (2020) - [Fashion Outfit Complementary Item Retrieval](https://mlsanity.com/paper/1912.08967/) (2019) - [Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramid](https://mlsanity.com/paper/1908.11754/) (2019) - [Visual Search at Alibaba](https://mlsanity.com/paper/2102.04674/) (2021) - [A Review of Modern Fashion Recommender Systems](https://mlsanity.com/paper/2202.02757/) (2022) - [Fashion Forward: Forecasting Visual Style in Fashion](https://mlsanity.com/paper/1705.06394/) (2017) - [Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning](https://mlsanity.com/paper/2208.12526/) (2022) - [Complete the Look: Scene-based Complementary Product Recommendation](https://mlsanity.com/paper/1812.01748/) (2018) - [GeoStyle: Discovering Fashion Trends and Events](https://mlsanity.com/paper/1908.11412/) (2019) - [Knowledge Enhanced Neural Fashion Trend Forecasting](https://mlsanity.com/paper/2005.03297/) (2020) - [Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network](https://mlsanity.com/paper/2002.02814/) (2020) - [Progressive Localization Networks for Language-based Moment Localization](https://mlsanity.com/paper/2102.01282/) (2021) - [Self-supervised Visual Attribute Learning for Fashion Compatibility](https://mlsanity.com/paper/2008.00348/) (2020) - [Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learning](https://mlsanity.com/paper/2104.02429/) (2021) - [SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval](https://mlsanity.com/paper/2209.13833/) (2022) - [Effectively Leveraging Attributes for Visual Similarity](https://mlsanity.com/paper/2105.01695/) (2021) - [TransNFCM: Translation-Based Neural Fashion Compatibility Modeling](https://mlsanity.com/paper/1812.10021/) (2018) - [Clothing Retrieval with Visual Attention Model](https://mlsanity.com/paper/1710.11446/) (2017) - [Fashion Image Retrieval with Capsule Networks](https://mlsanity.com/paper/1908.09943/) (2019) - [Hierarchical Similarity Learning for Language-based Product Image Retrieval](https://mlsanity.com/paper/2102.09375/) (2021) ## Built on by (most cited first, 9 of 9) - [Locate and Verify: A Two-Stream Network for Improved Deepfake Detection](https://mlsanity.com/paper/2309.11131/) (2023) - [Unified Multi-modal Unsupervised Representation Learning for Skeleton-based Action Understanding](https://mlsanity.com/paper/2311.03106/) (2023) - [Learning the Unlearned: Mitigating Feature Suppression in Contrastive Learning](https://mlsanity.com/paper/2402.11816/) (2024) - [AMELI: Enhancing Multimodal Entity Linking with Fine-Grained Attributes](https://mlsanity.com/paper/2305.14725/) (2023) - [Rethinking Weakly-supervised Video Temporal Grounding From a Game Perspective](https://mlsanity.com/paper/2605.26441/) (2026) - [Conditional Representation Learning for Customized Tasks](https://mlsanity.com/paper/2510.04564/) (2025) - [Fewer Steps, Better Performance: Efficient Cross-Modal Clip Trimming for Video Moment Retrieval Using Language](https://mlsanity.com/paper/2605.29793/) (2026) - [A Multihead Continual Learning Framework for Fine-Grained Fashion Image Retrieval with Contrastive Learning and Exponential Moving Average Distillation](https://mlsanity.com/paper/2603.20648/) (2026) - [Semantic Purification for Conditional Representation Learning](https://mlsanity.com/paper/2602.05464/) (2026) ## Abstract (arXiv) Attribute-specific fashion retrieval (ASFR) is a challenging information retrieval task, which has attracted increasing attention in recent years. Different from traditional fashion retrieval which mainly focuses on optimizing holistic similarity, the ASFR task concentrates on attribute-specific similarity, resulting in more fine-grained and interpretable retrieval results. As the attribute-specific similarity typically corresponds to the specific subtle regions of images, we propose a Region-to-Patch Framework (RPF) that consists of a region-aware branch and a patch-aware branch to extract fine-grained attribute-related visual features for precise retrieval in a coarse-to-fine manner. In particular, the region-aware branch is first to be utilized to locate the potential regions related to the semantic of the given attribute. Then, considering that the located region is coarse and still contains the background visual contents, the patch-aware branch is proposed to capture patch-wise attribute-related details from the previous amplified region. Such a hybrid architecture strikes a proper balance between region localization and feature extraction. Besides, different from previous works that solely focus on discriminating the attribute-relevant foreground visual features, we argue that the attribute-irrelevant background features are also crucial for distinguishing the detailed visual contexts in a contrastive manner. Therefore, a novel E-InfoNCE loss based on the foreground and background representations is further proposed to improve the discrimination of attribute-specific representation. Extensive experiments on three datasets demonstrate the effectiveness of our proposed framework, and also show a decent generalization of our RPF on out-of-domain fashion images. Our source code is available at https://github.com/HuiGuanLab/RPF.