400 citations · 652 across the 23 of their papers we have counts for
12 papers · 1 filter
Pay Attention and Move Better: Harnessing Attention for Interactive Motion Generation and Training-free Editing
Ling-Hao Chen, Shunlin Lu, Wenxun Dai +5
This research delves into the problem of interactive editing of human motion generation. Previous motion diffusion models lack explicit modeling of the word-level text-motion corre…
Spatial-Frequency Attention for Image Denoising
Shi Guo, Hongwei Yong, Xindong Zhang +2
The recently developed transformer networks have achieved impressive performance in image denoising by exploiting the self-attention (SA) in images. However, the existing methods m…
Explicit Box Detection Unifies End-to-End Multi-Person Pose Estimation
Jie Yang, Ailing Zeng, Shilong Liu +3
This paper presents a novel end-to-end framework with Explicit box Detection for multi-person Pose estimation, called ED-Pose, where it unifies the contextual learning between huma…
Adversarial Style Augmentation for Domain Generalization
Yabin Zhang, Bin Deng, Ruihuang Li +2
It is well-known that the performance of well-trained deep neural networks may degrade significantly when they are applied to data with even slightly shifted distributions. Recent…
A Survey on Leveraging Pre-trained Generative Adversarial Networks for Image Editing and Restoration
Ming Liu, Yuxiang Wei, Xiaohe Wu +2
Generative adversarial networks (GANs) have drawn enormous attention due to the simple yet effective training mechanism and superior image generation quality. With the ability to g…
Box-supervised Instance Segmentation with Level Set Evolution
Wentong Li, Wenyu Liu, Jianke Zhu +3
In contrast to the fully supervised methods using pixel-wise mask labels, box-supervised instance segmentation takes advantage of the simple box annotations, which has recently att…