Jingwen Wang

Orcid: 0009-0002-4704-1988

Affiliations:
  • Huya, Guangzhou, China
  • Tencent, China (former)
  • South China University of Technology, Guangzhou, China (PhD)


According to our database1, Jingwen Wang authored at least 13 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2023
MARN: Multi-level Attentional Reconstruction Networks for Weakly Supervised Video Temporal Grounding.
Neurocomputing, October, 2023

2022
Recurrent Exposure Generation for Low-Light Face Detection.
IEEE Trans. Multim., 2022

Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos.
IEEE Trans. Pattern Anal. Mach. Intell., 2022

2020
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study.
IEEE Trans. Image Process., 2020

Deep Bilateral Retinex for Low-Light Image Enhancement.
CoRR, 2020

STH: Spatio-Temporal Hybrid Convolution for Efficient Action Recognition.
CoRR, 2020

Weakly-Supervised Multi-Level Attentional Reconstruction Network for Grounding Textual Queries in Videos.
CoRR, 2020

Controllable Video Captioning with an Exemplar Sentence.
Proceedings of the MM '20: The 28th ACM International Conference on Multimedia, 2020

Beyond Monocular Deraining: Stereo Image Deraining via Semantic Understanding.
Proceedings of the Computer Vision - ECCV 2020, 2020

Temporally Grounding Language Queries in Videos by Contextual Boundary-Aware Prediction.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Controllable Video Captioning With POS Sequence Guidance Based on Gated Fusion Network.
Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision, 2019

2018
Non-local NetVLAD Encoding for Video Classification.
Proceedings of the Computer Vision - ECCV 2018 Workshops, 2018

Bidirectional Attentive Fusion With Context Gating for Dense Video Captioning.
Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition, 2018


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