Wei Wang, Tao Kong, Lei Li, Liang Wang, Tieniu Tan, Ya Jing
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erates the segmentation result with a light-weight segmentation network. Our LTS is simple but surprisingly effective. On three popular benchmark datasets, the LTS outperforms all the previous state-of-the-arts methods by a large margin (e.g., +3.2% on RefCOCO+ and +3.4% on RefCOCOg). In addition, our model is more interpretable with explicitly locating the object, which is also proved by visualization experiments. We believe this framework is promising to serve as a strong baseline for referring image segmentation. * This work was done when Ya Jing was an intern at ByteDance AI Lab.
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