52 citations · 180 across the 13 of their papers we have counts for
13 papers
Boosting Infrared Small Target Detection via Logit-Domain Contrast and Adaptive Shape Refinement
Handong Zeng, Zhengeng Yang, Shuai Zhang +2
Infrared small target detection (IRSTD) remains challenging due to tiny target size, low signal-to-noise ratio, severe foreground-background imbalance, and blurred boundaries in co…
Efficient Point Cloud Processing with High-Dimensional Positional Encoding and Non-Local MLPs
Yanmei Zou, Hongshan Yu, Yaonan Wang +4
Multi-Layer Perceptron (MLP) models are the foundation of contemporary point cloud processing. However, their complex network architectures obscure the source of their strength and…
Deep learning based infrared small object segmentation: Challenges and future directions
Zhengeng Yang, Hongshan Yu, Jianjun Zhang +2
Infrared sensing is a core method for supporting unmanned systems, such as autonomous vehicles and drones. Recently, infrared sensors have been widely deployed on mobile and statio…
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…
Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization
Hai-Ming Xu, Lingqiao Liu, Qiuchen Bian +1
Semi-supervised semantic segmentation requires the model to effectively propagate the label information from limited annotated images to unlabeled ones. A challenge for such a per-…
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,…
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…
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…