Papers › Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder
Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder
Zheyuan Liu, Weixuan Sun, Damien Teney, Stephen Gould
Composed image retrieval aims to find an image that best matches a given multi-modal user query consisting of a reference image and text pair. Existing methods commonly pre-compute image embeddings over the entire corpus and compare these to a reference image embedding modified by the query text at test time. Such a pipeline is very efficient at test time since fast vector distances can be used to evaluate candidates, but modifying the reference image embedding guided only by a short textual description can be difficult, especially independent of potential candidates. An alternative approach is to allow interactions between the query and every possible candidate, i.e., reference-text-candidate triplets, and pick the best from the entire set. Though this approach is more discriminative, for large-scale datasets the computational cost is prohibitive since pre-computation of candidate embeddings is no longer possible. We propose to combine the merits of both schemes using a two-stage model. Our first stage adopts the conventional vector distancing metric and performs a fast pruning among candidates. Meanwhile, our second stage employs a dual-encoder architecture, which effectively attends to the input triplet of reference-text-candidate and re-ranks the candidates. Both stages utilize a vision-and-language pre-trained network, which has proven beneficial for various downstream tasks. Our method consistently outperforms state-of-the-art approaches on standard benchmarks for the task. Our implementation is available at https://github.com/Cuberick-Orion/Candidate-Reranking-CIR.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Retrieval | CIRR | Candidate Set Re-ranking | (Recall@5+Recall_subset@1)/2 | 80.9 | #5 of 17 | Archive leaderboard | report |
| Image Retrieval | CIRR | Candidate Set Re-ranking | Recall@10 | 89.78 | #5 of 17 | Archive leaderboard | report |
| Image Retrieval | Fashion IQ | Candidate Set Re-ranking | (Recall@10+Recall@50)/2 | 62.15 | #5 of 22 | Archive leaderboard | report |
| Image Retrieval | Fashion IQ | Candidate Set Re-ranking | Recall@10 | 51.17 | #5 of 22 | 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.
Methods
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