In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation
<|reference_start|>In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic Segmentation: We present lazy visual grounding, a two-stage approach of unsupervised object mask discovery followed by object grounding, for open-vocabulary semantic segmentation. Plenty of the previous art casts this task as pixel-to-text classification without object-level comprehension, leveraging the image-to-text classification capability of pretrained vision-and-language models. We argue that visual objects are distinguishable without the prior text information as segmentation is essentially a vision task. Lazy visual grounding first discovers object masks covering an image with iterative Normalized cuts and then later assigns text on the discovered objects in a late interaction manner. Our model requires no additional training yet shows great performance on five public datasets: Pascal VOC, Pascal Context, COCO-object, COCO-stuff, and ADE 20K. Especially, the visually appealing segmentation results demonstrate the model capability to localize objects precisely. Paper homepage: https://cvlab.postech.ac.kr/research/lazygrounding<|reference_end|>
@article{kang2024in,
    title={In Defense of Lazy Visual Grounding for Open-Vocabulary Semantic
  Segmentation},
    author={Dahyun Kang and Minsu Cho},
    journal={arXiv preprint arXiv:2408.04961},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04961},
    primaryClass={cs.CV}
}