59 citations · 164 across the 11 of their papers we have counts for
11 papers
OST: Refining Text Knowledge with Optimal Spatio-Temporal Descriptor for General Video Recognition
Tongjia Chen, Hongshan Yu, Zhengeng Yang +3
Due to the resource-intensive nature of training vision-language models on expansive video data, a majority of studies have centered on adapting pre-trained image-language models t…
First Place Solution to the CVPR'2023 AQTC Challenge: A Function-Interaction Centric Approach with Spatiotemporal Visual-Language Alignment
Tom Tongjia Chen, Hongshan Yu, Zhengeng Yang +6
Affordance-Centric Question-driven Task Completion (AQTC) has been proposed to acquire knowledge from videos to furnish users with comprehensive and systematic instructions. Howeve…
DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds
Yong He, Hongshan Yu, Zhengeng Yang +3
Local features and contextual dependencies are crucial for 3D point cloud analysis. Many works have been devoted to designing better local convolutional kernels that exploit the co…
Full Point Encoding for Local Feature Aggregation in 3D Point Clouds
Yong He, Hongshan Yu, Zhengeng Yang +3
Point cloud processing methods exploit local point features and global context through aggregation which does not explicity model the internal correlations between local and global…
Domain-invariant Prototypes for Semantic Segmentation
Zhengeng Yang, Hongshan Yu, Wei Sun +2
Deep Learning has greatly advanced the performance of semantic segmentation, however, its success relies on the availability of large amounts of annotated data for training. Hence,…
Learning from Pixel-Level Noisy Label : A New Perspective for Light Field Saliency Detection
Mingtao Feng, Kendong Liu, Liang Zhang +3
Saliency detection with light field images is becoming attractive given the abundant cues available, however, this comes at the expense of large-scale pixel level annotated data wh…
Deep Learning Based 3D Segmentation: A Survey
Yong He, Hongshan Yu, Xiaoyan Liu +4
3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics. It has received significant attention from the com…
Self-supervised Learning with Fully Convolutional Networks
Zhengeng Yang, Hongshan Yu, Yong He +2
Although deep learning based methods have achieved great success in many computer vision tasks, their performance relies on a large number of densely annotated samples that are typ…
PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features
Qiang Fu, Jialong Wang, Hongshan Yu +4
Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraints on scene structure.…
Fast ORB-SLAM without Keypoint Descriptors
Qiang Fu, Hongshan Yu, Xiaolong Wang +4
Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 \cite{orbslam2} is a benchmark method in this domain, however…
Real time backbone for semantic segmentation
Zhengeng Yang, Hongshan Yu, Qiang Fu +4
The rapid development of autonomous driving in recent years presents lots of challenges for scene understanding. As an essential step towards scene understanding, semantic segmenta…