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of 284
pro vyhledávání: '"Kumar, B. V. K. Vijaya"'
Although a significant progress has been witnessed in supervised person re-identification (re-id), it remains challenging to generalize re-id models to new domains due to the huge domain gaps. Recently, there has been a growing interest in using unsu
Externí odkaz:
http://arxiv.org/abs/2007.10315
The human visual system uses numerous cues for depth perception, including disparity, accommodation, motion parallax and occlusion. It is incumbent upon virtual-reality displays to satisfy these cues to provide an immersive user experience. Multifoca
Externí odkaz:
http://arxiv.org/abs/2005.00946
Monocular Depth Estimation is usually treated as a supervised and regression problem when it actually is very similar to semantic segmentation task since they both are fundamentally pixel-level classification tasks. We applied depth increments that i
Externí odkaz:
http://arxiv.org/abs/1910.10369
Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation. These methods often involve an iterative process of predicting on target domain and then taking the confident predictions
Externí odkaz:
http://arxiv.org/abs/1908.09822
This paper considers the problem of image set-based face verification and identification. Unlike traditional single sample (an image or a video) setting, this situation assumes the availability of a set of heterogeneous collection of orderless images
Externí odkaz:
http://arxiv.org/abs/1908.01872
This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogeneous contents containing orderless images and videos. The importance o
Externí odkaz:
http://arxiv.org/abs/1907.03030
Recent deep networks achieved state of the art performance on a variety of semantic segmentation tasks. Despite such progress, these models often face challenges in real world `wild tasks' where large difference between labeled training/source data a
Externí odkaz:
http://arxiv.org/abs/1810.07911
Autor:
Yu, Zhiding, Liu, Weiyang, Zou, Yang, Feng, Chen, Ramalingam, Srikumar, Kumar, B. V. K. Vijaya, Kautz, Jan
Edge detection is among the most fundamental vision problems for its role in perceptual grouping and its wide applications. Recent advances in representation learning have led to considerable improvements in this area. Many state of the art edge dete
Externí odkaz:
http://arxiv.org/abs/1808.01992