activity
20182020
most citedDeep Bilateral Retinex for Low-Light Image Enhancement

16 citations · 27 across the 3 of their papers we have counts for

collaborators

10 papers

cs.CV202011 cited

Recurrent Exposure Generation for Low-Light Face Detection

Jinxiu Liang, Jingwen Wang, Yuhui Quan +4

Face detection from low-light images is challenging due to limited photos and inevitable noise, which, to make the task even harder, are often spatially unevenly distributed. A nat…

eess.IV202016 cited

Deep Bilateral Retinex for Low-Light Image Enhancement

Jinxiu Liang, Yong Xu, Yuhui Quan +3

Low-light images, i.e. the images captured in low-light conditions, suffer from very poor visibility caused by low contrast, color distortion and significant measurement noise. Low…

cs.CV2020

STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition

Xu Li, Jingwen Wang, Lin Ma +4

Effective and Efficient spatio-temporal modeling is essential for action recognition. Existing methods suffer from the trade-off between model performance and model complexity. In…

cs.CV2020

Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos

Yijun Song, Jingwen Wang, Lin Ma +2

The task of temporally grounding textual queries in videos is to localize one video segment that semantically corresponds to the given query. Most of the existing approaches rely o…

cs.CV2019

Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos

Yitian Yuan, Lin Ma, Jingwen Wang +2

Temporal sentence grounding in videos aims to detect and localize one target video segment, which semantically corresponds to a given sentence. Existing methods mainly tackle this…

cs.CV2019

Temporally Grounding Language Queries in Videos by Contextual Boundary-aware Prediction

Jingwen Wang, Lin Ma, Wenhao Jiang

The task of temporally grounding language queries in videos is to temporally localize the best matched video segment corresponding to a given language (sentence). It requires certa…