502 citations · 1.4k across the 190 of their papers we have counts for
10 papers · 1 filter
HRFormer: High-Resolution Transformer for Dense Prediction
Yuhui Yuan, Rao Fu, Lang Huang +4
We present a High-Resolution Transformer (HRFormer) that learns high-resolution representations for dense prediction tasks, in contrast to the original Vision Transformer that prod…
Realistic Image Synthesis with Configurable 3D Scene Layouts
Jaebong Jeong, Janghun Jo, Jingdong Wang +2
Recent conditional image synthesis approaches provide high-quality synthesized images. However, it is still challenging to accurately adjust image contents such as the positions an…
Conditional DETR for Fast Training Convergence
Depu Meng, Xiaokang Chen, Zejia Fan +5
The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the c…
Cross-Modal Attention Consistency for Video-Audio Unsupervised Learning
Shaobo Min, Qi Dai, Hongtao Xie +3
Cross-modal correlation provides an inherent supervision for video unsupervised representation learning. Existing methods focus on distinguishing different video clips by visual an…
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
Xiaokang Chen, Yuhui Yuan, Gang Zeng +1
In this paper, we study the semi-supervised semantic segmentation problem via exploring both labeled data and extra unlabeled data. We propose a novel consistency regularization ap…
On the Connection between Local Attention and Dynamic Depth-wise Convolution
Qi Han, Zejia Fan, Qi Dai +4
Vision Transformer (ViT) attains state-of-the-art performance in visual recognition, and the variant, Local Vision Transformer, makes further improvements. The major component in L…
Lite-HRNet: A Lightweight High-Resolution Network
Changqian Yu, Bin Xiao, Changxin Gao +4
We present an efficient high-resolution network, Lite-HRNet, for human pose estimation. We start by simply applying the efficient shuffle block in ShuffleNet to HRNet (high-resolut…
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
Zigang Geng, Ke Sun, Bin Xiao +2
In this paper, we are interested in the bottom-up paradigm of estimating human poses from an image. We study the dense keypoint regression framework that is previously inferior to…
Boosting Adversarial Transferability through Enhanced Momentum
Xiaosen Wang, Jiadong Lin, Han Hu +2
Deep learning models are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations on benign images. Many existing adversarial attack metho…
Learning Versatile Neural Architectures by Propagating Network Codes
Mingyu Ding, Yuqi Huo, Haoyu Lu +5
This work explores how to design a single neural network capable of adapting to multiple heterogeneous vision tasks, such as image segmentation, 3D detection, and video recognition…