Papers › Compositional Learning of Image-Text Query for Image Retrieval
Compositional Learning of Image-Text Query for Image Retrieval
Muhammad Umer Anwaar, Egor Labintcev, Martin Kleinsteuber
In this paper, we investigate the problem of retrieving images from a database based on a multi-modal (image-text) query. Specifically, the query text prompts some modification in the query image and the task is to retrieve images with the desired modifications. For instance, a user of an E-Commerce platform is interested in buying a dress, which should look similar to her friend's dress, but the dress should be of white color with a ribbon sash. In this case, we would like the algorithm to retrieve some dresses with desired modifications in the query dress. We propose an autoencoder based model, ComposeAE, to learn the composition of image and text query for retrieving images. We adopt a deep metric learning approach and learn a metric that pushes composition of source image and text query closer to the target images. We also propose a rotational symmetry constraint on the optimization problem. Our approach is able to outperform the state-of-the-art method TIRG \cite{TIRG} on three benchmark datasets, namely: MIT-States, Fashion200k and Fashion IQ. In order to ensure fair comparison, we introduce strong baselines by enhancing TIRG method. To ensure reproducibility of the results, we publish our code here: \url{https://github.com/ecom-research/ComposeAE}.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | Fashion IQ | ComposeAE | (Recall@10+Recall@50)/2 | 20.6 | #22 of 22 | Archive leaderboard | report |
| Image Retrieval with Multi-Modal Query | Fashion200k | ComposeAE | Recall@1 | 22.8 | #2 of 8 | Archive leaderboard | report |
| Image Retrieval with Multi-Modal Query | Fashion200k | ComposeAE | Recall@10 | 55.3 | #2 of 8 | Archive leaderboard | report |
| Image Retrieval with Multi-Modal Query | Fashion200k | ComposeAE | Recall@50 | 73.4 | #2 of 8 | Archive leaderboard | report |
| Image Retrieval with Multi-Modal Query | FashionIQ | ComposeAE | Recall@10 | 11.8 | #1 of 2 | Archive leaderboard | report |
| Image Retrieval with Multi-Modal Query | MIT-States | ComposeAE | Recall@1 | 13.9 | #1 of 5 | Archive leaderboard | report |
| Image Retrieval with Multi-Modal Query | MIT-States | ComposeAE | Recall@10 | 47.9 | #1 of 5 | Archive leaderboard | report |
| Image Retrieval with Multi-Modal Query | MIT-States | ComposeAE | Recall@5 | 35.5 | #1 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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