ScanFormer: Referring Expression Comprehension by Iteratively Scanning
- URL: http://arxiv.org/abs/2406.18048v1
- Date: Wed, 26 Jun 2024 03:56:03 GMT
- Title: ScanFormer: Referring Expression Comprehension by Iteratively Scanning
- Authors: Wei Su, Peihan Miao, Huanzhang Dou, Xi Li,
- Abstract summary: Referring Expression (REC) aims to localize the target objects specified by free-form natural language descriptions in images.
While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries.
This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model?
- Score: 11.95137121280909
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Referring Expression Comprehension (REC) aims to localize the target objects specified by free-form natural language descriptions in images. While state-of-the-art methods achieve impressive performance, they perform a dense perception of images, which incorporates redundant visual regions unrelated to linguistic queries, leading to additional computational overhead. This inspires us to explore a question: can we eliminate linguistic-irrelevant redundant visual regions to improve the efficiency of the model? Existing relevant methods primarily focus on fundamental visual tasks, with limited exploration in vision-language fields. To address this, we propose a coarse-to-fine iterative perception framework, called ScanFormer. It can iteratively exploit the image scale pyramid to extract linguistic-relevant visual patches from top to bottom. In each iteration, irrelevant patches are discarded by our designed informativeness prediction. Furthermore, we propose a patch selection strategy for discarded patches to accelerate inference. Experiments on widely used datasets, namely RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame, verify the effectiveness of our method, which can strike a balance between accuracy and efficiency.
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