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Click-Feedback Retrieval

arXiv 2305.00052 ↗First posted 28 Apr 2023cs.CVcited by 0

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Abstract (arXiv)

Retrieving target information based on input query is of fundamental importance in many real-world applications. In practice, it is not uncommon for the initial search to fail, where additional feedback information is needed to guide the searching process. In this work, we study a setting where the feedback is provided through users clicking liked and disliked searching results. We believe this form of feedback is of great practical interests for its convenience and efficiency. To facilitate future work in this direction, we construct a new benchmark termed click-feedback retrieval based on a large-scale dataset in fashion domain. We demonstrate that incorporating click-feedback can drastically improve the retrieval performance, which validates the value of the proposed setting. We also introduce several methods to utilize click-feedback during training, and show that click-feedback-guided training can significantly enhance the retrieval quality. We hope further exploration in this direction can bring new insights on building more efficient and user-friendly search engines.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 28 Apr 2023.

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24 of 41 references.

References · 24

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2019ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Taskscited by 2,219
  3. 2021Align before Fuse: Vision and Language Representation Learning with Momentum Distillationcited by 1,612
  4. 2021ViLT: Vision-and-Language Transformer Without Convolution or Region Supervisioncited by 1,224
  5. 2021FILIP: Fine-grained Interactive Language-Image Pre-Trainingcited by 626
  6. 2018Stacked Cross Attention for Image-Text Matchingcited by 414
  7. 2020Pixel-BERT: Aligning Image Pixels with Text by Deep Multi-Modal Transformerscited by 288
  8. 2018Composing Text and Image for Image Retrieval - An Empirical Odysseycited by 234
  9. 2021Probabilistic Embeddings for Cross-Modal Retrievalcited by 150
  10. 2017Dual-Path Convolutional Image-Text Embeddings with Instance Losscited by 145
  11. 2017Automatic Spatially-aware Fashion Concept Discoverycited by 132
  12. 2017Learning Two-Branch Neural Networks for Image-Text Matching Taskscited by 119
12 more
  1. 2019CAMP: Cross-Modal Adaptive Message Passing for Text-Image Retrievalcited by 110
  2. 2018Dialog-based Interactive Image Retrievalcited by 84
  3. 2021Playing Lottery Tickets with Vision and Languagecited by 45
  4. 2019Fashion IQ: A New Dataset Towards Retrieving Images by Natural Language Feedbackcited by 42
  5. 2022FashionViL: Fashion-Focused Vision-and-Language Representation Learningcited by 32
  6. 2021ROSITA: Enhancing Vision-and-Language Semantic Alignments via Cross- and Intra-modal Knowledge Integrationcited by 31
  7. 2019Fashion Retrieval via Graph Reasoning Networks on a Similarity Pyramidcited by 28
  8. 2022A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketchcited by 28
  9. 2022Multi-Query Video Retrievalcited by 11
  10. 2021Ask&Confirm: Active Detail Enriching for Cross-Modal Retrieval with Partial Querycited by 9
  11. 2019Thinking Outside the Pool: Active Training Image Creation for Relative Attributescited by 5
  12. 2017HashGAN:Attention-aware Deep Adversarial Hashing for Cross Modal Retrievalcited by 3

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