mlsanityData to 7 Oct 2026

Image Search with Text Feedback by Additive Attention Compositional Learning

arXiv 2203.03809 ↗First posted 8 Mar 2022cs.CV cs.CLcited by 7

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Abstract (arXiv)

Effective image retrieval with text feedback stands to impact a range of real-world applications, such as e-commerce. Given a source image and text feedback that describes the desired modifications to that image, the goal is to retrieve the target images that resemble the source yet satisfy the given modifications by composing a multi-modal (image-text) query. We propose a novel solution to this problem, Additive Attention Compositional Learning (AACL), that uses a multi-modal transformer-based architecture and effectively models the image-text contexts. Specifically, we propose a novel image-text composition module based on additive attention that can be seamlessly plugged into deep neural networks. We also introduce a new challenging benchmark derived from the Shopping100k dataset. AACL is evaluated on three large-scale datasets (FashionIQ, Fashion200k, and Shopping100k), each with strong baselines. Extensive experiments show that AACL achieves new state-of-the-art results on all three datasets.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 43 of its 57 references that are in the map. First posted on arXiv 8 Mar 2022.

Builds on

43 of 57 references.

References · 43

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31 more
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Built on by

Cited by year

Citing papers per year: 2023: 4; 2024: 1; 2025: 2.

7 papers.

  1. 2023CompoDiff: Versatile Composed Image Retrieval With Latent Diffusioncited by 84
  2. 2023Language-only Efficient Training of Zero-shot Composed Image Retrievalcited by 27
  3. 2023Dynamic Weighted Combiner for Mixed-Modal Image Retrievalcited by 15
  4. 2023Que2Engage: Embedding-based Retrieval for Relevant and Engaging Products at Facebook Marketplacecited by 9
  5. 2025Multimodal Reasoning Agent for Zero-Shot Composed Image Retrievalcited by 7
  6. 2024A Survey of Multimodal Composite Editing and Retrievalcited by 5
  7. 2025MLLM-Guided VLM Fine-Tuning with Joint Inference for Zero-Shot Composed Image Retrievalcited by 5