148 citations · 259 across the 16 of their papers we have counts for
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Co-Evolution of Pose and Mesh for 3D Human Body Estimation from Video
Yingxuan You, Hong Liu, Ti Wang +3
Despite significant progress in single image-based 3D human mesh recovery, accurately and smoothly recovering 3D human motion from a video remains challenging. Existing video-based…
A Gated Cross-domain Collaborative Network for Underwater Object Detection
Linhui Dai, Hong Liu, Pinhao Song +1
Underwater object detection (UOD) plays a significant role in aquaculture and marine environmental protection. Considering the challenges posed by low contrast and low-light condit…
Interweaved Graph and Attention Network for 3D Human Pose Estimation
Ti Wang, Hong Liu, Runwei Ding +3
Despite substantial progress in 3D human pose estimation from a single-view image, prior works rarely explore global and local correlations, leading to insufficient learning of hum…
GATOR: Graph-Aware Transformer with Motion-Disentangled Regression for Human Mesh Recovery from a 2D Pose
Yingxuan You, Hong Liu, Xia Li +3
3D human mesh recovery from a 2D pose plays an important role in various applications. However, it is hard for existing methods to simultaneously capture the multiple relations dur…
Feature Completion Transformer for Occluded Person Re-identification
Tao Wang, Mengyuan Liu, Hong Liu +4
Occluded person re-identification (Re-ID) is a challenging problem due to the destruction of occluders. Most existing methods focus on visible human body parts through some prior i…
HTNet: Human Topology Aware Network for 3D Human Pose Estimation
Jialun Cai, Hong Liu, Runwei Ding +3
3D human pose estimation errors would propagate along the human body topology and accumulate at the end joints of limbs. Inspired by the backtracking mechanism in automatic control…
Boosting R-CNN: Reweighting R-CNN Samples by RPN's Error for Underwater Object Detection
Pinhao Song, Pengteng Li, Linhui Dai +2
Complicated underwater environments bring new challenges to object detection, such as unbalanced light conditions, low contrast, occlusion, and mimicry of aquatic organisms. Under…
Pose-guided Feature Disentangling for Occluded Person Re-identification Based on Transformer
Tao Wang, Hong Liu, Pinhao Song +2
Occluded person re-identification is a challenging task as human body parts could be occluded by some obstacles (e.g. trees, cars, and pedestrians) in certain scenes. Some existing…
Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition
Tianyu Guo, Hong Liu, Zhan Chen +3
In recent years, self-supervised representation learning for skeleton-based action recognition has been developed with the advance of contrastive learning methods. The existing con…
MHFormer: Multi-Hypothesis Transformer for 3D Human Pose Estimation
Wenhao Li, Hong Liu, Hao Tang +2
Estimating 3D human poses from monocular videos is a challenging task due to depth ambiguity and self-occlusion. Most existing works attempt to solve both issues by exploiting spat…
Transformer for Single Image Super-Resolution
Zhisheng Lu, Juncheng Li, Hong Liu +3
Single image super-resolution (SISR) has witnessed great strides with the development of deep learning. However, most existing studies focus on building more complex networks with…
Weakly-supervised 3D Human Pose Estimation with Cross-view U-shaped Graph Convolutional Network
Guoliang Hua, Hong Liu, Wenhao Li +3
Although monocular 3D human pose estimation methods have made significant progress, it is far from being solved due to the inherent depth ambiguity. Instead, exploiting multi-view…
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning
Yang Chen, Pinhao Song, Hong Liu +4
The performance of existing underwater object detection methods degrades seriously when facing domain shift caused by complicated underwater environments. Due to the limitation of…
Exploiting Temporal Contexts with Strided Transformer for 3D Human Pose Estimation
Wenhao Li, Hong Liu, Runwei Ding +3
Despite the great progress in 3D human pose estimation from videos, it is still an open problem to take full advantage of a redundant 2D pose sequence to learn representative repre…
Multi-Scale Cascading Network with Compact Feature Learning for RGB-Infrared Person Re-Identification
Can Zhang, Hong Liu, Wei Guo +1
RGB-Infrared person re-identification (RGB-IR Re-ID) aims to match persons from heterogeneous images captured by visible and thermal cameras, which is of great significance in the…