34 citations · 82 across the 21 of their papers we have counts for
21 papers
Towards Deeply Unified Depth-aware Panoptic Segmentation with Bi-directional Guidance Learning
Junwen He, Yifan Wang, Lijun Wang +6
Depth-aware panoptic segmentation is an emerging topic in computer vision which combines semantic and geometric understanding for more robust scene interpretation. Recent works pur…
PoSynDA: Multi-Hypothesis Pose Synthesis Domain Adaptation for Robust 3D Human Pose Estimation
Hanbing Liu, Jun-Yan He, Zhi-Qi Cheng +8
Existing 3D human pose estimators face challenges in adapting to new datasets due to the lack of 2D-3D pose pairs in training sets. To overcome this issue, we propose \textit{Multi…
Tracking Anything in High Quality
Jiawen Zhu, Zhenyu Chen, Zeqi Hao +9
Visual object tracking is a fundamental video task in computer vision. Recently, the notably increasing power of perception algorithms allows the unification of single/multiobject…
Point-Voxel Absorbing Graph Representation Learning for Event Stream based Recognition
Yuxiang Zhang, Chengguo Yuan, Xiao Wang +2
Sampled point and voxel methods are usually employed to downsample the dense events into sparse ones. After that, one popular way is to leverage a graph model which treats the spar…
KeyPosS: Plug-and-Play Facial Landmark Detection through GPS-Inspired True-Range Multilateration
Xu Bao, Zhi-Qi Cheng, Jun-Yan He +8
Accurate facial landmark detection is critical for facial analysis tasks, yet prevailing heatmap and coordinate regression methods grapple with prohibitive computational costs and…
Overcoming Topology Agnosticism: Enhancing Skeleton-Based Action Recognition through Redefined Skeletal Topology Awareness
Yuxuan Zhou, Zhi-Qi Cheng, Jun-Yan He +3
Graph Convolutional Networks (GCNs) have long defined the state-of-the-art in skeleton-based action recognition, leveraging their ability to unravel the complex dynamics of human j…
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
Hui Lv, Zhongqi Yue, Qianru Sun +3
Weakly Supervised Video Anomaly Detection (WSVAD) is challenging because the binary anomaly label is only given on the video level, but the output requires snippet-level prediction…
DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving
Jun-Yan He, Zhi-Qi Cheng, Chenyang Li +5
Real-time perception, or streaming perception, is a crucial aspect of autonomous driving that has yet to be thoroughly explored in existing research. To address this gap, we presen…
HDFormer: High-order Directed Transformer for 3D Human Pose Estimation
Hanyuan Chen, Jun-Yan He, Wangmeng Xiang +6
Human pose estimation is a challenging task due to its structured data sequence nature. Existing methods primarily focus on pair-wise interaction of body joints, which is insuffici…
Adversarial Example Defense via Perturbation Grading Strategy
Shaowei Zhu, Wanli Lyu, Bin Li +2
Deep Neural Networks have been widely used in many fields. However, studies have shown that DNNs are easily attacked by adversarial examples, which have tiny perturbations and grea…
Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer based Approach
Xixi Wang, Bo Jiang, Xiao Wang +1
Exploring sample relationships within each mini-batch has shown great potential for learning image representations. Existing works generally adopt the regular Transformer to model…
Data Dimension Reduction makes ML Algorithms efficient
Wisal Khan, Muhammad Turab, Waqas Ahmad +3
Data dimension reduction (DDR) is all about mapping data from high dimensions to low dimensions, various techniques of DDR are being used for image dimension reduction like Random…
LongShortNet: Exploring Temporal and Semantic Features Fusion in Streaming Perception
Chenyang Li, Zhi-Qi Cheng, Jun-Yan He +6
Streaming perception is a critical task in autonomous driving that requires balancing the latency and accuracy of the autopilot system. However, current methods for streaming perce…
ProContEXT: Exploring Progressive Context Transformer for Tracking
Jin-Peng Lan, Zhi-Qi Cheng, Jun-Yan He +6
Existing Visual Object Tracking (VOT) only takes the target area in the first frame as a template. This causes tracking to inevitably fail in fast-changing and crowded scenes, as i…
Few-Shot Learning Meets Transformer: Unified Query-Support Transformers for Few-Shot Classification
Xixi Wang, Xiao Wang, Bo Jiang +1
Few-shot classification which aims to recognize unseen classes using very limited samples has attracted more and more attention. Usually, it is formulated as a metric learning prob…
Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code
Changan Niu, Chuanyi Li, Bin Luo +1
Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has ena…
Unified GCNs: Towards Connecting GCNs with CNNs
Ziyan Zhang, Bo Jiang, Bin Luo
Graph Convolutional Networks (GCNs) have been widely demonstrated their powerful ability in graph data representation and learning. Existing graph convolution layers are mainly des…
Tiny Object Tracking: A Large-scale Dataset and A Baseline
Yabin Zhu, Chenglong Li, Yao Liu +4
Tiny objects, frequently appearing in practical applications, have weak appearance and features, and receive increasing interests in meany vision tasks, such as object detection an…
Universal adversarial perturbation for remote sensing images
Qingyu Wang, Guorui Feng, Zhaoxia Yin +1
Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditi…
Residual Objectness for Imbalance Reduction
Joya Chen, Dong Liu, Bin Luo +3
For a long time, object detectors have suffered from extreme imbalance between foregrounds and backgrounds. While several sampling/reweighting schemes have been explored to allevia…
Attributes Guided Feature Learning for Vehicle Re-identification
Hongchao Li, Xianmin Lin, Aihua Zheng +4
Vehicle Re-ID has recently attracted enthusiastic attention due to its potential applications in smart city and urban surveillance. However, it suffers from large intra-class varia…