73 citations · 173 across the 6 of their papers we have counts for
6 papers
Referring Transformer: A One-step Approach to Multi-task Visual Grounding
Muchen Li, Leonid Sigal
As an important step towards visual reasoning, visual grounding (e.g., phrase localization, referring expression comprehension/segmentation) has been widely explored Previous appro…
Learning Spatial and Spatio-Temporal Pixel Aggregations for Image and Video Denoising
Xiangyu Xu, Muchen Li, Wenxiu Sun +1
Existing denoising methods typically restore clear results by aggregating pixels from the noisy input. Instead of relying on hand-crafted aggregation schemes, we propose to explici…
TDAF: Top-Down Attention Framework for Vision Tasks
Bo Pang, Yizhuo Li, Jiefeng Li +3
Human attention mechanisms often work in a top-down manner, yet it is not well explored in vision research. Here, we propose the Top-Down Attention Framework (TDAF) to capture top-…
NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
Dario Fuoli, Zhiwu Huang, Martin Danelljan +18
This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain. The chall…
TubeTK: Adopting Tubes to Track Multi-Object in a One-Step Training Model
Bo Pang, Yizhuo Li, Yifan Zhang +2
Multi-object tracking is a fundamental vision problem that has been studied for a long time. As deep learning brings excellent performances to object detection algorithms, Tracking…
Learning Deformable Kernels for Image and Video Denoising
Xiangyu Xu, Muchen Li, Wenxiu Sun
Most of the classical denoising methods restore clear results by selecting and averaging pixels in the noisy input. Instead of relying on hand-crafted selecting and averaging strat…