Zobrazeno 1 - 10
of 202
pro vyhledávání: '"Li-zhong Yu"'
Vision representation learning, especially self-supervised learning, is pivotal for various vision applications. Ensemble learning has also succeeded in enhancing the performance and robustness of the vision models. However, traditional ensemble stra
Externí odkaz:
http://arxiv.org/abs/2411.15787
Masked image modeling has achieved great success in learning representations but is limited by the huge computational costs. One cost-saving strategy makes the decoder reconstruct only a subset of masked tokens and throw the others, and we refer to t
Externí odkaz:
http://arxiv.org/abs/2411.15746
Existing object detection methods often consider sRGB input, which was compressed from RAW data using ISP originally designed for visualization. However, such compression might lose crucial information for detection, especially under complex light an
Externí odkaz:
http://arxiv.org/abs/2411.15678
Incorporating heterogeneous representations from different architectures has facilitated various vision tasks, e.g., some hybrid networks combine transformers and convolutions. However, complementarity between such heterogeneous architectures has not
Externí odkaz:
http://arxiv.org/abs/2310.05108
Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. Large receptive fields facilitate long-term relations, while small receptive fields help to capture the local details. Existing methods construct models w
Externí odkaz:
http://arxiv.org/abs/2206.06637
Learning representations with self-supervision for convolutional networks (CNN) has been validated to be effective for vision tasks. As an alternative to CNN, vision transformers (ViT) have strong representation ability with spatial self-attention an
Externí odkaz:
http://arxiv.org/abs/2206.05184
Publikováno v:
In Industrial Crops & Products 15 December 2024 222 Part 3
Publikováno v:
PLoS ONE, Vol 7, Iss 6, p e39502 (2012)
Promoting the seed regeneration potential of secondary forests undergoing gap disturbances is an important approach for achieving forest restoration and sustainable management. Seedling recruitment from seed banks strongly determines the seed regener
Externí odkaz:
https://doaj.org/article/d400e456182649c29a66564512e33dc5
Publikováno v:
IEEE TPAMI 2022
Empowered by large datasets, e.g., ImageNet, unsupervised learning on large-scale data has enabled significant advances for classification tasks. However, whether the large-scale unsupervised semantic segmentation can be achieved remains unknown. The
Externí odkaz:
http://arxiv.org/abs/2106.03149
Publikováno v:
CVPR 2021
Temporal receptive fields of models play an important role in action segmentation. Large receptive fields facilitate the long-term relations among video clips while small receptive fields help capture the local details. Existing methods construct mod
Externí odkaz:
http://arxiv.org/abs/2101.00910