Zobrazeno 1 - 10
of 31
pro vyhledávání: '"Changqian Yu"'
Publikováno v:
CAAI Transactions on Intelligence Technology, Vol 9, Iss 1, Pp 87-100 (2024)
Abstract Since the fully convolutional network has achieved great success in semantic segmentation, lots of works have been proposed to extract discriminative pixel representations. However, the authors observe that existing methods still suffer from
Externí odkaz:
https://doaj.org/article/4506b2ad08934b96a875adb82d768154
Publikováno v:
IEEE Transactions on Cybernetics. 53:1641-1652
Human parsing is a fine-grained semantic segmentation task, which needs to understand human semantic parts. Most existing methods model human parsing as a general semantic segmentation, which ignores the inherent relationship among hierarchical human
Publikováno v:
IEEE Transactions on Multimedia. :1-12
Publikováno v:
International Journal of Computer Vision. 129:3051-3068
Low-level details and high-level semantics are both essential to the semantic segmentation task. However, to speed up the model inference, current approaches almost always sacrifice the low-level details, leading to a considerable decrease in accurac
Publikováno v:
Neurocomputing. 453:885-895
Weakly-supervised semantic segmentation based on image-level annotations has difficulty exploring pixel-level information. Most approaches adopt Class Activation Maps (CAM) to localize initial object regions, called seeds. To cover more potential obj
The fully convolutional network (FCN) has achieved tremendous success in dense visual recognition tasks, such as scene segmentation. The last layer of FCN is typically a global classifier (1x1 convolution) to recognize each pixel to a semantic label.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::50856c8e627d0acc42b5e13cb5f600b9
http://arxiv.org/abs/2109.10322
http://arxiv.org/abs/2109.10322
Publikováno v:
Pattern Recognition. 126:108544
Publikováno v:
CVPR
We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolution network), yielding stronger performance over popular lightweight n
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::727d56d3e4aad0f193e31372e0f2ae60
http://arxiv.org/abs/2104.06403
http://arxiv.org/abs/2104.06403
Image manipulation with StyleGAN has been an increasing concern in recent years.Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit the attributes of the generated images.However, due to the limited seman
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::773dcf7869e927712f9203fd6192df95
Publikováno v:
CVPR
Recent works have widely explored the contextual dependencies to achieve more accurate segmentation results. However, most approaches rarely distinguish different types of contextual dependencies, which may pollute the scene understanding. In this wo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bb1a67bf6fab3b4db7886cfc69a865a4
http://arxiv.org/abs/2004.01547
http://arxiv.org/abs/2004.01547