25 citations · 25 across the 6 of their papers we have counts for
7 papers
AMIEOD: Adaptive Multi-Experts Image Enhancement for Object Detection in Low-Illumination Scenes
Xiaochen Huang, Honggang Chen, Weicheng Zhang +4
In multimedia application scenarios, images captured under low-illumination conditions often lead to lower accuracy in visual perception tasks compared to those taken in well-lit e…
Model Compression using Progressive Channel Pruning
Jinyang Guo, Weichen Zhang, Wanli Ouyang +1
In this work, we propose a simple but effective channel pruning framework called Progressive Channel Pruning (PCP) to accelerate Convolutional Neural Networks (CNNs). In contrast t…
Progressive Modality Cooperation for Multi-Modality Domain Adaptation
Weichen Zhang, Dong Xu, Jing Zhang +1
In this work, we propose a new generic multi-modality domain adaptation framework called Progressive Modality Cooperation (PMC) to transfer the knowledge learned from the source do…
Self-Paced Collaborative and Adversarial Network for Unsupervised Domain Adaptation
Weichen Zhang, Dong Xu, Wanli Ouyang +1
This paper proposes a new unsupervised domain adaptation approach called Collaborative and Adversarial Network (CAN), which uses the domain-collaborative and domain-adversarial lea…
MA-NeRF: Motion-Assisted Neural Radiance Fields for Face Synthesis from Sparse Images
Weichen Zhang, Xiang Zhou, Yukang Cao +2
We address the problem of photorealistic 3D face avatar synthesis from sparse images. Existing Parametric models for face avatar reconstruction struggle to generate details that or…
Towards Arbitrary Text-driven Image Manipulation via Space Alignment
Yunpeng Bai, Zihan Zhong, Chao Dong +3
The recent GAN inversion methods have been able to successfully invert the real image input to the corresponding editable latent code in StyleGAN. By combining with the language-vi…
Maximum-Margin Structured Learning with Deep Networks for 3D Human Pose Estimation
Sijin Li, Weichen Zhang, Antoni B. Chan
This paper focuses on structured-output learning using deep neural networks for 3D human pose estimation from monocular images. Our network takes an image and 3D pose as inputs and…