Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866915037770153984 |
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| author | Wen, Haokun Song, Xuemeng Yin, Jianhua Wu, Jianlong Guan, Weili Nie, Liqiang |
| author_facet | Wen, Haokun Song, Xuemeng Yin, Jianhua Wu, Jianlong Guan, Weili Nie, Liqiang |
| contents | The composed image retrieval (CIR) task aims to retrieve the desired target image for a given multimodal query, i.e., a reference image with its corresponding modification text. The key limitations encountered by existing efforts are two aspects: 1) ignoring the multi-faceted query-target matching factors; 2) ignoring the potential unlabeled reference-target image pairs in existing benchmark datasets. To address these two limitations is non-trivial due to the following challenges: 1) how to effectively model the multi-faceted matching factors in a latent way without direct supervision signals; 2) how to fully utilize the potential unlabeled reference-target image pairs to improve the generalization ability of the CIR model. To address these challenges, in this work, we first propose a muLtI-faceted Matching Network (LIMN), which consists of three key modules: multi-grained image/text encoder, latent factor-oriented feature aggregation, and query-target matching modeling. Thereafter, we design an iterative dual self-training paradigm to further enhance the performance of LIMN by fully utilizing the potential unlabeled reference-target image pairs in a semi-supervised manner. Specifically, we denote the iterative dual self-training paradigm enhanced LIMN as LIMN+. Extensive experiments on three real-world datasets, FashionIQ, Shoes, and Birds-to-Words, show that our proposed method significantly surpasses the state-of-the-art baselines. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_09979 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval Wen, Haokun Song, Xuemeng Yin, Jianhua Wu, Jianlong Guan, Weili Nie, Liqiang Multimedia The composed image retrieval (CIR) task aims to retrieve the desired target image for a given multimodal query, i.e., a reference image with its corresponding modification text. The key limitations encountered by existing efforts are two aspects: 1) ignoring the multi-faceted query-target matching factors; 2) ignoring the potential unlabeled reference-target image pairs in existing benchmark datasets. To address these two limitations is non-trivial due to the following challenges: 1) how to effectively model the multi-faceted matching factors in a latent way without direct supervision signals; 2) how to fully utilize the potential unlabeled reference-target image pairs to improve the generalization ability of the CIR model. To address these challenges, in this work, we first propose a muLtI-faceted Matching Network (LIMN), which consists of three key modules: multi-grained image/text encoder, latent factor-oriented feature aggregation, and query-target matching modeling. Thereafter, we design an iterative dual self-training paradigm to further enhance the performance of LIMN by fully utilizing the potential unlabeled reference-target image pairs in a semi-supervised manner. Specifically, we denote the iterative dual self-training paradigm enhanced LIMN as LIMN+. Extensive experiments on three real-world datasets, FashionIQ, Shoes, and Birds-to-Words, show that our proposed method significantly surpasses the state-of-the-art baselines. |
| title | Self-Training Boosted Multi-Factor Matching Network for Composed Image Retrieval |
| topic | Multimedia |
| url | https://arxiv.org/abs/2305.09979 |