mlsanityData to 7 Oct 2026

Automatic Spatially-aware Fashion Concept Discovery

arXiv 1708.01311 ↗First posted 3 Aug 2017cs.CVcited by 132

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Abstract (arXiv)

This paper proposes an automatic spatially-aware concept discovery approach using weakly labeled image-text data from shopping websites. We first fine-tune GoogleNet by jointly modeling clothing images and their corresponding descriptions in a visual-semantic embedding space. Then, for each attribute (word), we generate its spatially-aware representation by combining its semantic word vector representation with its spatial representation derived from the convolutional maps of the fine-tuned network. The resulting spatially-aware representations are further used to cluster attributes into multiple groups to form spatially-aware concepts (e.g., the neckline concept might consist of attributes like v-neck, round-neck, etc). Finally, we decompose the visual-semantic embedding space into multiple concept-specific subspaces, which facilitates structured browsing and attribute-feedback product retrieval by exploiting multimodal linguistic regularities. We conducted extensive experiments on our newly collected Fashion200K dataset, and results on clustering quality evaluation and attribute-feedback product retrieval task demonstrate the effectiveness of our automatically discovered spatially-aware concepts.

Key facts. Cited by 132 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Aug 2017.

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Citing papers per year: 2021: 10; 2022: 17; 2023: 26; 2024: 29; 2025: 27; 2026: 23.

132 papers.

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  1. 2021Image Retrieval on Real-life Images with Pre-trained Vision-and-Language Modelscited by 244
  2. 2024VisRAG: Vision-based Retrieval-augmented Generation on Multi-modality Documentscited by 237
  3. 2023Zero-Shot Composed Image Retrieval with Textual Inversioncited by 171
  4. 2024GME: Improving Universal Multimodal Retrieval by Multimodal LLMscited by 166
  5. 2023UniIR: Training and Benchmarking Universal Multimodal Information Retrieverscited by 165
  6. 2024MM-Embed: Universal Multimodal Retrieval with Multimodal LLMscited by 159
  7. 2023Vision-by-Language for Training-Free Compositional Image Retrievalcited by 102
  8. 2023CompoDiff: Versatile Composed Image Retrieval With Latent Diffusioncited by 84
  9. 2022ARTEMIS: Attention-based Retrieval with Text-Explicit Matching and Implicit Similaritycited by 82
  10. 2023Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructionscited by 76
  11. 2023Multimodal Garment Designer: Human-Centric Latent Diffusion Models for Fashion Image Editingcited by 62
  12. 2023Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Featurescited by 52
38 more
  1. 2023GeneCIS: A Benchmark for General Conditional Image Similaritycited by 46
  2. 2022Composed Image Retrieval with Text Feedback via Multi-grained Uncertainty Regularizationcited by 45
  3. 2023Data Roaming and Quality Assessment for Composed Image Retrievalcited by 45
  4. 2024Spherical Linear Interpolation and Text-Anchoring for Zero-shot Composed Image Retrievalcited by 44
  5. 2023Bi-directional Training for Composed Image Retrieval via Text Prompt Learningcited by 43
  6. 2025FineCIR: Explicit Parsing of Fine-Grained Modification Semantics for Composed Image Retrievalcited by 41
  7. 2023Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encodercited by 35
  8. 2022FashionViL: Fashion-Focused Vision-and-Language Representation Learningcited by 32
  9. 2021Conversational Fashion Image Retrieval via Multiturn Natural Language Feedbackcited by 29
  10. 2022Probabilistic Compositional Embeddings for Multimodal Image Retrievalcited by 29
  11. 2023S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captionscited by 29
  12. 2022A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketchcited by 28
  13. 2022Open-vocabulary Attribute Detectioncited by 28
  14. 2024iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrievalcited by 24
  15. 2025Retrieval-Augmented Perception: High-Resolution Image Perception Meets Visual RAGcited by 22
  16. 2021Embedding Arithmetic of Multimodal Queries for Image Retrievalcited by 21
  17. 2024CaLa: Complementary Association Learning for Augmenting Composed Image Retrievalcited by 21
  18. 2024EgoCVR: An Egocentric Benchmark for Fine-Grained Composed Video Retrievalcited by 21
  19. 2021Analogous to Evolutionary Algorithm: Designing a Unified Sequence Modelcited by 20
  20. 2023FreestyleRet: Retrieving Images from Style-Diversified Queriescited by 19
  21. 2024Enhancing Interactive Image Retrieval With Query Rewriting Using Large Language Models and Vision Language Modelscited by 19
  22. 2024UniFashion: A Unified Vision-Language Model for Multimodal Fashion Retrieval and Generationcited by 17
  23. 2025A Comprehensive Survey on Composed Image Retrievalcited by 17
  24. 2024Matryoshka-Adaptor: Unsupervised and Supervised Tuning for Smaller Embedding Dimensionscited by 16
  25. 2023Dynamic Weighted Combiner for Mixed-Modal Image Retrievalcited by 15
  26. 2023Collaborative Group: Composed Image Retrieval via Consensus Learning from Noisy Annotationscited by 14
  27. 2024Composed Image Retrieval for Remote Sensingcited by 14
  28. 2024Advancing Myopia To Holism: Fully Contrastive Language-Image Pre-trainingcited by 13
  29. 2025SMEC: Rethinking Matryoshka Representation Learning for Retrieval Embedding Compressioncited by 13
  30. 2021Fine-Grained Fashion Similarity Prediction by Attribute-Specific Embedding Learningcited by 12
  31. 2023FashionNTM: Multi-turn Fashion Image Retrieval via Cascaded Memorycited by 11
  32. 2024Leveraging Large Language Models for Multimodal Searchcited by 11
  33. 2025MIEB: Massive Image Embedding Benchmarkcited by 11
  34. 2023Graph Neural Networks in Vision-Language Image Understanding: A Surveycited by 10
  35. 2023Search-Adaptor: Embedding Customization for Information Retrievalcited by 10
  36. 2024Test-time Adaptation for Cross-modal Retrieval with Query Shiftcited by 10
  37. 2025U-MARVEL: Unveiling Key Factors for Universal Multimodal Retrieval via Embedding Learning with MLLMscited by 10
  38. 2025UniECS: Unified Multimodal E-Commerce Search Framework with Gated Cross-modal Fusioncited by 10

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