Fully Aligned Network for Referring Image Segmentation
<|reference_start|>Fully Aligned Network for Referring Image Segmentation: 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.<|reference_end|>
@article{liu2024fully,
    title={Fully Aligned Network for Referring Image Segmentation},
    author={Yong Liu, Ruihao Xu, Yansong Tang},
    journal={arXiv preprint arXiv:2409.19569},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.19569},
    primaryClass={cs.CV}
}