Fuse & Calibrate: A bi-directional Vision-Language Guided Framework for Referring Image Segmentation

• URL: http://arxiv.org/abs/2405.11205v1
• Date: Sat, 18 May 2024 07:21:12 GMT
• Title: Fuse & Calibrate: A bi-directional Vision-Language Guided Framework for Referring Image Segmentation
• Authors: Yichen Yan, Xingjian He, Sihan Chen, Shichen Lu, Jing Liu,
• Abstract summary: We introduce FCNet, a framework that employs a bi-directional guided fusion approach where both vision and language play guiding roles. Specifically, we use a vision-guided approach to conduct initial multi-modal fusion, obtaining multi-modal features that focus on key vision information. We then propose a language-guided calibration module to further calibrate these multi-modal features, ensuring they understand the context of the input sentence.
• Score: 8.383431263616105
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring Image Segmentation (RIS) aims to segment an object described in natural language from an image, with the main challenge being a text-to-pixel correlation. Previous methods typically rely on single-modality features, such as vision or language features, to guide the multi-modal fusion process. However, this approach limits the interaction between vision and language, leading to a lack of fine-grained correlation between the language description and pixel-level details during the decoding process. In this paper, we introduce FCNet, a framework that employs a bi-directional guided fusion approach where both vision and language play guiding roles. Specifically, we use a vision-guided approach to conduct initial multi-modal fusion, obtaining multi-modal features that focus on key vision information. We then propose a language-guided calibration module to further calibrate these multi-modal features, ensuring they understand the context of the input sentence. This bi-directional vision-language guided approach produces higher-quality multi-modal features sent to the decoder, facilitating adaptive propagation of fine-grained semantic information from textual features to visual features. Experiments on RefCOCO, RefCOCO+, and G-Ref datasets with various backbones consistently show our approach outperforming state-of-the-art methods.

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