In this paper, we address the space-time video super-resolution, which aims at generating a high-resolution (HR) slow-motion video from a low-resolution (LR) and low frame rate (LFR) video sequence. A naïve method is to decompose it into two sub-tasks: video frame interpolation (VFI) and video super-resolution (VSR). Nevertheless, temporal interpolation and spatial upscaling are intra-related in this problem. Two-stage approaches cannot fully make use of this natural property. Besides, state-of-the-art VFI or VSR deep networks usually have a large frame reconstruction module in order to obtain high-quality photo-realistic video frames, which makes the two-stage approaches have large models and thus be relatively time-consuming. To overcome the issues, we present a one-stage space-time video super-resolution framework, which can directly reconstruct an HR slow-motion video sequence from an input LR and LFR video. Instead of reconstructing missing LR intermediate frames as VFI models do, we temporally interpolate LR frame features of the missing LR frames capturing local temporal contexts by a feature temporal interpolation module. Extensive experiments on widely used benchmarks demonstrate that the proposed framework not only achieves better qualitative and quantitative performance on both clean and noisy LR frames but also is several times faster than recent state-of-the-art two-stage networks. The source code is released in https://github.com/Mukosame/Zooming-Slow-Mo-CVPR-2020 .
Zoom in, Click out: Unlocking and Evaluating the Potential of Zooming for GUI Grounding
Xi’an Jiaotong University（西安交通大学）；Princeton University（普林斯顿大学）；Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）；Michigan State University（密歇根州立大学）
Dual Arbitrary Scale Super-Resolution for Multi-Contrast MRI
Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；The Hong Kong Polytechnic University（香港理工大学）；The University of Texas at Dallas（德克萨斯大学达拉斯分校）
Adaptive Chain-of-Focus Reasoning via Dynamic Visual Search and Zooming for Efficient VLMs
organization= School of Computer Science \& Technology, Beijing Institute of Technology , city= Beijing , country= China；organization= State Key Laboratory of General Artificial Intelligence, BIGAI , city= Beijing , country= China；organization= School of Intelligence Science；Technology, Peking University , city= Beijing , country= China；organization= Guangdong Laboratory of Machine Perception；Intelligent Computing, Shenzhen MSU--BIT University , city= Shenzhen , country= China；organization= Department of Automation, Tsinghua University , city= Beijing , country= China
