Make Graph-based Referring Expression Comprehension Great Again through Expression-guided Dynamic Gating and Regression
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arXiv
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| Format: | Preprint |
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2024
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| author | Ke, Jingcheng Wang, Dele Chen, Jun-Cheng Jhuo, I-Hong Lin, Chia-Wen Lin, Yen-Yu |
| author_facet | Ke, Jingcheng Wang, Dele Chen, Jun-Cheng Jhuo, I-Hong Lin, Chia-Wen Lin, Yen-Yu |
| contents | One common belief is that with complex models and pre-training on large-scale datasets, transformer-based methods for referring expression comprehension (REC) perform much better than existing graph-based methods. We observe that since most graph-based methods adopt an off-the-shelf detector to locate candidate objects (i.e., regions detected by the object detector), they face two challenges that result in subpar performance: (1) the presence of significant noise caused by numerous irrelevant objects during reasoning, and (2) inaccurate localization outcomes attributed to the provided detector. To address these issues, we introduce a plug-and-adapt module guided by sub-expressions, called dynamic gate constraint (DGC), which can adaptively disable irrelevant proposals and their connections in graphs during reasoning. We further introduce an expression-guided regression strategy (EGR) to refine location prediction. Extensive experimental results on the RefCOCO, RefCOCO+, RefCOCOg, Flickr30K, RefClef, and Ref-reasoning datasets demonstrate the effectiveness of the DGC module and the EGR strategy in consistently boosting the performances of various graph-based REC methods. Without any pretaining, the proposed graph-based method achieves better performance than the state-of-the-art (SOTA) transformer-based methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03385 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Make Graph-based Referring Expression Comprehension Great Again through Expression-guided Dynamic Gating and Regression Ke, Jingcheng Wang, Dele Chen, Jun-Cheng Jhuo, I-Hong Lin, Chia-Wen Lin, Yen-Yu Computer Vision and Pattern Recognition Multimedia One common belief is that with complex models and pre-training on large-scale datasets, transformer-based methods for referring expression comprehension (REC) perform much better than existing graph-based methods. We observe that since most graph-based methods adopt an off-the-shelf detector to locate candidate objects (i.e., regions detected by the object detector), they face two challenges that result in subpar performance: (1) the presence of significant noise caused by numerous irrelevant objects during reasoning, and (2) inaccurate localization outcomes attributed to the provided detector. To address these issues, we introduce a plug-and-adapt module guided by sub-expressions, called dynamic gate constraint (DGC), which can adaptively disable irrelevant proposals and their connections in graphs during reasoning. We further introduce an expression-guided regression strategy (EGR) to refine location prediction. Extensive experimental results on the RefCOCO, RefCOCO+, RefCOCOg, Flickr30K, RefClef, and Ref-reasoning datasets demonstrate the effectiveness of the DGC module and the EGR strategy in consistently boosting the performances of various graph-based REC methods. Without any pretaining, the proposed graph-based method achieves better performance than the state-of-the-art (SOTA) transformer-based methods. |
| title | Make Graph-based Referring Expression Comprehension Great Again through Expression-guided Dynamic Gating and Regression |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2409.03385 |