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pro vyhledávání: '"Kwon, Hyeongjun"'
Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation
Source-Free domain adaptive Object Detection (SFOD) is a promising strategy for deploying trained detectors to new, unlabeled domains without accessing source data, addressing significant concerns around data privacy and efficiency. Most SFOD methods
Externí odkaz:
http://arxiv.org/abs/2407.13524
Visual scenes are naturally organized in a hierarchy, where a coarse semantic is recursively comprised of several fine details. Exploring such a visual hierarchy is crucial to recognize the complex relations of visual elements, leading to a comprehen
Externí odkaz:
http://arxiv.org/abs/2404.00974
Given the inevitability of domain shifts during inference in real-world applications, test-time adaptation (TTA) is essential for model adaptation after deployment. However, the real-world scenario of continuously changing target distributions presen
Externí odkaz:
http://arxiv.org/abs/2311.05858
Recent DETR-based video grounding models have made the model directly predict moment timestamps without any hand-crafted components, such as a pre-defined proposal or non-maximum suppression, by learning moment queries. However, their input-agnostic
Externí odkaz:
http://arxiv.org/abs/2308.06947
Recent progress in deterministic prompt learning has become a promising alternative to various downstream vision tasks, enabling models to learn powerful visual representations with the help of pre-trained vision-language models. However, this approa
Externí odkaz:
http://arxiv.org/abs/2304.00779
We address the problem of few-shot semantic segmentation (FSS), which aims to segment novel class objects in a target image with a few annotated samples. Though recent advances have been made by incorporating prototype-based metric learning, existing
Externí odkaz:
http://arxiv.org/abs/2111.04982
The multi-volume set of LNCS books with volume numbers 15059 upto 15147 constitutes the refereed proceedings of the 18th European Conference on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024. The 2387 papers p