68 citations · 285 across the 36 of their papers we have counts for
36 papers
Semi-DETR: Semi-Supervised Object Detection with Detection Transformers
Jiacheng Zhang, Xiangru Lin, Wei Zhang +6
We analyze the DETR-based framework on semi-supervised object detection (SSOD) and observe that (1) the one-to-one assignment strategy generates incorrect matching when the pseudo…
CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching
Jincheng Lu, Xipeng Yang, Jin Ye +4
Multi-Camera Multi-Target Tracking (MCMT) is a computer vision technique that involves tracking multiple targets simultaneously across multiple cameras. MCMT in urban traffic visua…
Multi-Modal 3D Object Detection by Box Matching
Zhe Liu, Xiaoqing Ye, Zhikang Zou +5
Multi-modal 3D object detection has received growing attention as the information from different sensors like LiDAR and cameras are complementary. Most fusion methods for 3D detect…
ByteTrackV2: 2D and 3D Multi-Object Tracking by Associating Every Detection Box
Yifu Zhang, Xinggang Wang, Xiaoqing Ye +6
Multi-object tracking (MOT) aims at estimating bounding boxes and identities of objects across video frames. Detection boxes serve as the basis of both 2D and 3D MOT. The inevitabl…
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection
Chang Liu, Weiming Zhang, Xiangru Lin +6
With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that…
CAPE: Camera View Position Embedding for Multi-View 3D Object Detection
Kaixin Xiong, Shi Gong, Xiaoqing Ye +5
In this paper, we address the problem of detecting 3D objects from multi-view images. Current query-based methods rely on global 3D position embeddings (PE) to learn the geometric…
StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection
Zhe Liu, Xiaoqing Ye, Xiao Tan +2
In this paper, we propose a cross-modal distillation method named StereoDistill to narrow the gap between the stereo and LiDAR-based approaches via distilling the stereo detectors…
Repainting and Imitating Learning for Lane Detection
Yue He, Minyue Jiang, Xiaoqing Ye +5
Current lane detection methods are struggling with the invisibility lane issue caused by heavy shadows, severe road mark degradation, and serious vehicle occlusion. As a result, di…
SoccerNet 2022 Challenges Results
Silvio Giancola, Anthony Cioppa, Adrien Deliège +91
The SoccerNet 2022 challenges were the second annual video understanding challenges organized by the SoccerNet team. In 2022, the challenges were composed of 6 vision-based tasks:…
Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation
Peizhe Jiang, Wei Yang, Xiaoqing Ye +2
Monocular depth estimation (MDE) in the self-supervised scenario has emerged as a promising method as it refrains from the requirement of ground truth depth. Despite continuous eff…
Spatial Pruned Sparse Convolution for Efficient 3D Object Detection
Jianhui Liu, Yukang Chen, Xiaoqing Ye +3
3D scenes are dominated by a large number of background points, which is redundant for the detection task that mainly needs to focus on foreground objects. In this paper, we analyz…
AGO-Net: Association-Guided 3D Point Cloud Object Detection Network
Liang Du, Xiaoqing Ye, Xiao Tan +5
The human brain can effortlessly recognize and localize objects, whereas current 3D object detection methods based on LiDAR point clouds still report inferior performance for detec…
Paint and Distill: Boosting 3D Object Detection with Semantic Passing Network
Bo Ju, Zhikang Zou, Xiaoqing Ye +4
3D object detection task from lidar or camera sensors is essential for autonomous driving. Pioneer attempts at multi-modality fusion complement the sparse lidar point clouds with r…
Neural Deformable Voxel Grid for Fast Optimization of Dynamic View Synthesis
Xiang Guo, Guanying Chen, Yuchao Dai +4
Recently, Neural Radiance Fields (NeRF) is revolutionizing the task of novel view synthesis (NVS) for its superior performance. In this paper, we propose to synthesize dynamic scen…
GitNet: Geometric Prior-based Transformation for Birds-Eye-View Segmentation
Shi Gong, Xiaoqing Ye, Xiao Tan +4
Birds-eye-view (BEV) semantic segmentation is critical for autonomous driving for its powerful spatial representation ability. It is challenging to estimate the BEV semantic maps f…
Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task
Xiaoqing Ye, Mao Shu, Hanyu Li +5
Concurrent perception datasets for autonomous driving are mainly limited to frontal view with sensors mounted on the vehicle. None of them is designed for the overlooked roadside p…
The Devil is in the Task: Exploiting Reciprocal Appearance-Localization Features for Monocular 3D Object Detection
Zhikang Zou, Xiaoqing Ye, Liang Du +6
Low-cost monocular 3D object detection plays a fundamental role in autonomous driving, whereas its accuracy is still far from satisfactory. In this paper, we dig into the 3D object…
SGM3D: Stereo Guided Monocular 3D Object Detection
Zheyuan Zhou, Liang Du, Xiaoqing Ye +5
Monocular 3D object detection aims to predict the object location, dimension and orientation in 3D space alongside the object category given only a monocular image. It poses a grea…
Weakly-Supervised Spatio-Temporal Anomaly Detection in Surveillance Video
Jie Wu, Wei Zhang, Guanbin Li +5
In this paper, we introduce a novel task, referred to as Weakly-Supervised Spatio-Temporal Anomaly Detection (WSSTAD) in surveillance video. Specifically, given an untrimmed video,…
Good Practices and A Strong Baseline for Traffic Anomaly Detection
Yuxiang Zhao, Wenhao Wu, Yue He +3
The detection of traffic anomalies is a critical component of the intelligent city transportation management system. Previous works have proposed a variety of notable insights and…
DSANet: Dynamic Segment Aggregation Network for Video-Level Representation Learning
Wenhao Wu, Yuxiang Zhao, Yanwu Xu +8
Long-range and short-range temporal modeling are two complementary and crucial aspects of video recognition. Most of the state-of-the-arts focus on short-range spatio-temporal mode…
Coherent Loss: A Generic Framework for Stable Video Segmentation
Mingyang Qian, Yi Fu, Xiao Tan +5
Video segmentation approaches are of great importance for numerous vision tasks especially in video manipulation for entertainment. Due to the challenges associated with acquiring…
Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching
Di Hu, Rui Qian, Minyue Jiang +5
Discriminatively localizing sounding objects in cocktail-party, i.e., mixed sound scenes, is commonplace for humans, but still challenging for machines. In this paper, we propose a…
Face Sketch Synthesis with Style Transfer using Pyramid Column Feature
Chaofeng Chen, Xiao Tan, Kwan-Yee K. Wong
In this paper, we propose a novel framework based on deep neural networks for face sketch synthesis from a photo. Imitating the process of how artists draw sketches, our framework…