Fully Aligned Network for Referring Image Segmentation

• URL: http://arxiv.org/abs/2409.19569v1
• Date: Sun, 29 Sep 2024 06:13:34 GMT
• Title: Fully Aligned Network for Referring Image Segmentation
• Authors: Yong Liu, Ruihao Xu, Yansong Tang,
• Abstract summary: This paper focuses on the Referring Image task, which aims to segment objects from an image based on a given language description. The critical problem of RIS is achieving fine-grained alignment between different modalities to recognize and segment the target object. We present a Fully Aligned Network (FAN) that follows four cross-modal interaction principles.
• Score: 22.40918154209717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper focuses on the Referring Image Segmentation (RIS) task, which aims to segment objects from an image based on a given language description. The critical problem of RIS is achieving fine-grained alignment between different modalities to recognize and segment the target object. Recent advances using the attention mechanism for cross-modal interaction have achieved excellent progress. However, current methods tend to lack explicit principles of interaction design as guidelines, leading to inadequate cross-modal comprehension. Additionally, most previous works use a single-modal mask decoder for prediction, losing the advantage of full cross-modal alignment. To address these challenges, we present a Fully Aligned Network (FAN) that follows four cross-modal interaction principles. Under the guidance of reasonable rules, our FAN achieves state-of-the-art performance on the prevalent RIS benchmarks (RefCOCO, RefCOCO+, G-Ref) with a simple architecture.

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