CoHD: A Counting-Aware Hierarchical Decoding Framework for Generalized Referring Expression Segmentation

• URL: http://arxiv.org/abs/2405.15658v2
• Date: Mon, 25 Nov 2024 17:14:20 GMT
• Title: CoHD: A Counting-Aware Hierarchical Decoding Framework for Generalized Referring Expression Segmentation
• Authors: Zhuoyan Luo, Yinghao Wu, Tianheng Cheng, Yong Liu, Yicheng Xiao, Hongfa Wang, Xiao-Ping Zhang, Yujiu Yang,
• Abstract summary: Generalized Referring Expression (GRES) amplifies the formulation of classic RES by involving complex multiple/non-target scenarios. Recent approaches address GRES by directly extending the well-adopted RES frameworks with object-existence identification. We propose a textbfCounting-Aware textbfHierarchical textbfDecoding framework (CoHD) for GRES.
• Score: 37.96005100341482
• License:
• Abstract: The newly proposed Generalized Referring Expression Segmentation (GRES) amplifies the formulation of classic RES by involving complex multiple/non-target scenarios. Recent approaches address GRES by directly extending the well-adopted RES frameworks with object-existence identification. However, these approaches tend to encode multi-granularity object information into a single representation, which makes it difficult to precisely represent comprehensive objects of different granularity. Moreover, the simple binary object-existence identification across all referent scenarios fails to specify their inherent differences, incurring ambiguity in object understanding. To tackle the above issues, we propose a \textbf{Co}unting-Aware \textbf{H}ierarchical \textbf{D}ecoding framework (CoHD) for GRES. By decoupling the intricate referring semantics into different granularity with a visual-linguistic hierarchy, and dynamic aggregating it with intra- and inter-selection, CoHD boosts multi-granularity comprehension with the reciprocal benefit of the hierarchical nature. Furthermore, we incorporate the counting ability by embodying multiple/single/non-target scenarios into count- and category-level supervision, facilitating comprehensive object perception. Experimental results on gRefCOCO, Ref-ZOM, R-RefCOCO, and RefCOCO benchmarks demonstrate the effectiveness and rationality of CoHD which outperforms state-of-the-art GRES methods by a remarkable margin. Code is available at \href{https://github.com/RobertLuo1/CoHD}{here}.

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