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pro vyhledávání: '"Wang, Xixi"'
Event camera-based visual tracking has drawn more and more attention in recent years due to the unique imaging principle and advantages of low energy consumption, high dynamic range, and dense temporal resolution. Current event-based tracking algorit
Externí odkaz:
http://arxiv.org/abs/2408.10487
Current works focus on addressing the remote sensing change detection task using bi-temporal images. Although good performance can be achieved, however, seldom of they consider the motion cues which may also be vital. In this work, we revisit the wid
Externí odkaz:
http://arxiv.org/abs/2408.08078
Segment Anything Model (SAM) has recently achieved amazing results in the field of natural image segmentation. However, it is not effective for medical image segmentation, owing to the large domain gap between natural and medical images. In this pape
Externí odkaz:
http://arxiv.org/abs/2404.14837
Existing visual change detectors usually adopt CNNs or Transformers for feature representation learning and focus on learning effective representation for the changed regions between images. Although good performance can be obtained by enhancing the
Externí odkaz:
http://arxiv.org/abs/2310.11417
Exploring sample relationships within each mini-batch has shown great potential for learning image representations. Existing works generally adopt the regular Transformer to model the visual content relationships, ignoring the cues of semantic/label
Externí odkaz:
http://arxiv.org/abs/2211.10622
Few-shot classification which aims to recognize unseen classes using very limited samples has attracted more and more attention. Usually, it is formulated as a metric learning problem. The core issue of few-shot classification is how to learn (1) con
Externí odkaz:
http://arxiv.org/abs/2208.12398
Publikováno v:
In Environmental Pollution 1 November 2024 360
Autor:
Wang, Xixi, Chen, Jiani, Xu, Lei, Miao, Jie, Sunarso, Jaka, Wang, Xiaoyu, Cao, Wei, Yang, Yang, Zhou, Wei
Publikováno v:
In Journal of Power Sources 15 September 2024 614