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- Stanford UniversityUS33 papers
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32 papers · 2 filters
Self-supervised Learning of Motion Capture
Hsiao-Yu Fish Tung, Hsiao-Wei Tung, Ersin Yumer +1
Current state-of-the-art solutions for motion capture from a single camera are optimization driven: they optimize the parameters of a 3D human model so that its re-projection match…
Visual to Sound: Generating Natural Sound for Videos in the Wild
Yipin Zhou, Zhaowen Wang, Chen Fang +2
As two of the five traditional human senses (sight, hearing, taste, smell, and touch), vision and sound are basic sources through which humans understand the world. Often correlate…
Toward Multimodal Image-to-Image Translation
Jun-Yan Zhu, Richard Zhang, Deepak Pathak +4
Many image-to-image translation problems are ambiguous, as a single input image may correspond to multiple possible outputs. In this work, we aim to model a \emph{distribution} of…
Contextual-based Image Inpainting: Infer, Match, and Translate
Yuhang Song, Chao Yang, Zhe Lin +4
We study the task of image inpainting, which is to fill in the missing region of an incomplete image with plausible contents. To this end, we propose a learning-based approach to g…
NISP: Pruning Networks using Neuron Importance Score Propagation
Ruichi Yu, Ang Li, Chun-Fu Chen +6
To reduce the significant redundancy in deep Convolutional Neural Networks (CNNs), most existing methods prune neurons by only considering statistics of an individual layer or two…
C-WSL: Count-guided Weakly Supervised Localization
Mingfei Gao, Ang Li, Ruichi Yu +2
We introduce count-guided weakly supervised localization (C-WSL), an approach that uses per-class object count as a new form of supervision to improve weakly supervised localizatio…