HARIS: Human-Like Attention for Reference Image Segmentation

• URL: http://arxiv.org/abs/2405.10707v2
• Date: Tue, 21 May 2024 09:55:58 GMT
• Title: HARIS: Human-Like Attention for Reference Image Segmentation
• Authors: Mengxi Zhang, Heqing Lian, Yiming Liu, Jie Chen,
• Abstract summary: We propose a referring image segmentation method called HARIS, which introduces the Human-Like Attention mechanism. Our method achieves state-of-the-art performance and great zero-shot ability.
• Score: 5.808325471170541
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Referring image segmentation (RIS) aims to locate the particular region corresponding to the language expression. Existing methods incorporate features from different modalities in a \emph{bottom-up} manner. This design may get some unnecessary image-text pairs, which leads to an inaccurate segmentation mask. In this paper, we propose a referring image segmentation method called HARIS, which introduces the Human-Like Attention mechanism and uses the parameter-efficient fine-tuning (PEFT) framework. To be specific, the Human-Like Attention gets a \emph{feedback} signal from multi-modal features, which makes the network center on the specific objects and discard the irrelevant image-text pairs. Besides, we introduce the PEFT framework to preserve the zero-shot ability of pre-trained encoders. Extensive experiments on three widely used RIS benchmarks and the PhraseCut dataset demonstrate that our method achieves state-of-the-art performance and great zero-shot ability.

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