Similarly, we compare OVOSE with state-of-the-art methods designed for closed-set settings in unsupervised domain adaptation for event-based semantic segmentation. OVOSE demonstrates superior performance, showcasing its potential for real-world applications. The code is available at https://github....
Through our CAT-Seg framework, we fine- tune the encoders of CLIP for its adaptation for the down- stream task of segmentation. Our method surpasses the pre- vious state-of-the-art in standard benchmarks and also in scenarios with a vast domain difference. The s...
Universal source-free domain adaptation. In Proceed- ings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4544–4553, 2020. 5 [16] Brian Lester, Rami Al-Rfou, and Noah Constant. The power of scale for parameter-efficient prompt tuning. ar...
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