Applying ViT in Generalized Few-shot Semantic Segmentation
<|reference_start|>Applying ViT in Generalized Few-shot Semantic Segmentation: This paper explores the capability of ViT-based models under the generalized few-shot semantic segmentation (GFSS) framework. We conduct experiments with various combinations of backbone models, including ResNets and pretrained Vision Transformer (ViT)-based models, along with decoders featuring a linear classifier, UPerNet, and Mask Transformer. The structure made of DINOv2 and linear classifier takes the lead on popular few-shot segmentation bench mark PASCAL-$5^i$, substantially outperforming the best of ResNet structure by 116% in one-shot scenario. We demonstrate the great potential of large pretrained ViT-based model on GFSS task, and expect further improvement on testing benchmarks. However, a potential caveat is that when applying pure ViT-based model and large scale ViT decoder, the model is easy to overfit.<|reference_end|>
@article{geng2024applying,
    title={Applying ViT in Generalized Few-shot Semantic Segmentation},
    author={Liyuan Geng, Jinhong Xia, Yuanhe Guo},
    journal={arXiv preprint arXiv:2408.14957},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.14957},
    primaryClass={cs.CV}
}