Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder

• URL: http://arxiv.org/abs/2305.16304v3
• Date: Mon, 29 Jan 2024 05:03:55 GMT
• Title: Candidate Set Re-ranking for Composed Image Retrieval with Dual Multi-modal Encoder
• Authors: Zheyuan Liu, Weixuan Sun, Damien Teney, Stephen Gould
• Abstract summary: 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 pre-compute image embeddings over the entire corpus and compare these to a reference image embedding modified by the query text at test time. We propose to combine the merits of both schemes using a two-stage model.
• Score: 45.60134971181856
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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