Fashion-IQ 2020 Challenge 2nd Place Team's Solution
Not on the map. Why?
Abstract (arXiv)
This paper is dedicated to team VAA's approach submitted to the Fashion-IQ challenge in CVPR 2020. Given a pair of the image and the text, we present a novel multimodal composition method, RTIC, that can effectively combine the text and the image modalities into a semantic space. We extract the image and the text features that are encoded by the CNNs and the sequential models (e.g., LSTM or GRU), respectively. To emphasize the meaning of the residual of the feature between the target and candidate, the RTIC is composed of N-blocks with channel-wise attention modules. Then, we add the encoded residual to the feature of the candidate image to obtain a synthesized feature. We also explored an ensemble strategy with variants of models and achieved a significant boost in performance comparing to the best single model. Finally, our approach achieved 2nd place in the Fashion-IQ 2020 Challenge with a test score of 48.02 on the leaderboard.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Jul 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 2; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
4 papers.
- 2021Conversational Fashion Image Retrieval via Multiturn Natural Language Feedbackcited by 29
- 2021RTIC: Residual Learning for Text and Image Composition using Graph Convolutional Networkcited by 27
- 2022Image Search with Text Feedback by Additive Attention Compositional Learningcited by 7
- 2025Composed Multi-modal Retrieval: A Survey of Approaches and Applicationscited by 2
- 2025Composed Multi-modal Retrieval: A Survey of Approaches and Applications3 Mar 2025
- 2022Image Search with Text Feedback by Additive Attention Compositional Learning8 Mar 2022
- 2021Conversational Fashion Image Retrieval via Multiturn Natural Language Feedback8 Jun 2021
- 2021RTIC: Residual Learning for Text and Image Composition using Graph Convolutional Network7 Apr 2021