23 citations · 56 across the 8 of their papers we have counts for
8 papers
Delving into Cascaded Instability: A Lipschitz Continuity View on Image Restoration and Object Detection Synergy
Qing Zhao, Weijian Deng, Pengxu Wei +4
To improve detection robustness in adverse conditions (e.g., haze and low light), image restoration is commonly applied as a pre-processing step to enhance image quality for the de…
Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attention
Hannan Lu, Xiaohe Wu, Shudong Wang +5
Generating multi-view videos for autonomous driving training has recently gained much attention, with the challenge of addressing both cross-view and cross-frame consistency. Exist…
Evaluation of Text-to-Video Generation Models: A Dynamics Perspective
Mingxiang Liao, Hannan Lu, Xinyu Zhang +6
Comprehensive and constructive evaluation protocols play an important role in the development of sophisticated text-to-video (T2V) generation models. Existing evaluation protocols…
FaceScore: Benchmarking and Enhancing Face Quality in Human Generation
Zhenyi Liao, Qingsong Xie, Chen Chen +2
Diffusion models (DMs) have achieved significant success in generating imaginative images given textual descriptions. However, they are likely to fall short when it comes to real-l…
Two-Stream Networks for Object Segmentation in Videos
Hannan Lu, Zhi Tian, Lirong Yang +2
Existing matching-based approaches perform video object segmentation (VOS) via retrieving support features from a pixel-level memory, while some pixels may suffer from lack of corr…
AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results
Pengxu Wei, Hannan Lu, Radu Timofte +68
This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…
Component Divide-and-Conquer for Real-World Image Super-Resolution
Pengxu Wei, Ziwei Xie, Hannan Lu +4
In this paper, we present a large-scale Diverse Real-world image Super-Resolution dataset, i.e., DRealSR, as well as a divide-and-conquer Super-Resolution (SR) network, exploring t…
Blind Super-Resolution With Iterative Kernel Correction
Jinjin Gu, Hannan Lu, Wangmeng Zuo +1
Deep learning based methods have dominated super-resolution (SR) field due to their remarkable performance in terms of effectiveness and efficiency. Most of these methods assume th…