1.6k citations · 3.8k across the 68 of their papers we have counts for
70 papers
Condition-Adaptive Graph Convolution Learning for Skeleton-Based Gait Recognition
Xiaohu Huang, Xinggang Wang, Zhidianqiu Jin +4
Graph convolutional networks have been widely applied in skeleton-based gait recognition. A key challenge in this task is to distinguish the individual walking styles of different…
MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction
Bencheng Liao, Shaoyu Chen, Yunchi Zhang +5
High-definition (HD) map provides abundant and precise static environmental information of the driving scene, serving as a fundamental and indispensable component for planning in a…
Symphonize 3D Semantic Scene Completion with Contextual Instance Queries
Haoyi Jiang, Tianheng Cheng, Naiyu Gao +4
`3D Semantic Scene Completion (SSC) has emerged as a nascent and pivotal undertaking in autonomous driving, aiming to predict voxel occupancy within volumetric scenes. However, pre…
SparseTrack: Multi-Object Tracking by Performing Scene Decomposition based on Pseudo-Depth
Zelin Liu, Xinggang Wang, Cheng Wang +2
Exploring robust and efficient association methods has always been an important issue in multiple-object tracking (MOT). Although existing tracking methods have achieved impressive…
Matte Anything: Interactive Natural Image Matting with Segment Anything Models
Jingfeng Yao, Xinggang Wang, Lang Ye +1
Natural image matting algorithms aim to predict the transparency map (alpha-matte) with the trimap guidance. However, the production of trimap often requires significant labor, whi…
RPTQ: Reorder-based Post-training Quantization for Large Language Models
Zhihang Yuan, Lin Niu, Jiawei Liu +7
Large-scale language models (LLMs) have demonstrated impressive performance, but their deployment presents challenges due to their significant memory usage. This issue can be allev…
A Range-Null Space Decomposition Approach for Fast and Flexible Spectral Compressive Imaging
Junyu Wang, Shijie Wang, Ruijie Zhang +3
We present RND-SCI, a novel framework for compressive hyperspectral image (HSI) reconstruction. Our framework decomposes the reconstructed object into range-space and null-space co…
Improving Post-Training Quantization on Object Detection with Task Loss-Guided Lp Metric
Lin Niu, Jiawei Liu, Zhihang Yuan +3
Efficient inference for object detection networks is a major challenge on edge devices. Post-Training Quantization (PTQ), which transforms a full-precision model into low bit-width…
GaitGS: Temporal Feature Learning in Granularity and Span Dimension for Gait Recognition
Haijun Xiong, Yunze Deng, Bin Feng +2
Gait recognition, a growing field in biological recognition technology, utilizes distinct walking patterns for accurate individual identification. However, existing methods lack th…
TinyDet: Accurate Small Object Detection in Lightweight Generic Detectors
Shaoyu Chen, Tianheng Cheng, Jiemin Fang +4
Small object detection requires the detection head to scan a large number of positions on image feature maps, which is extremely hard for computation- and energy-efficient lightwei…
VMA: Divide-and-Conquer Vectorized Map Annotation System for Large-Scale Driving Scene
Shaoyu Chen, Yunchi Zhang, Bencheng Liao +7
High-definition (HD) map serves as the essential infrastructure of autonomous driving. In this work, we build up a systematic vectorized map annotation framework (termed VMA) for e…
WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation
Lianghui Zhu, Yingyue Li, Jiemin Fang +4
Transformer has been very successful in various computer vision tasks and understanding the working mechanism of transformer is important. As touchstones, weakly-supervised semanti…
Generalizable Neural Voxels for Fast Human Radiance Fields
Taoran Yi, Jiemin Fang, Xinggang Wang +1
Rendering moving human bodies at free viewpoints only from a monocular video is quite a challenging problem. The information is too sparse to model complicated human body structure…
Benchmarking the Reliability of Post-training Quantization: a Particular Focus on Worst-case Performance
Zhihang Yuan, Jiawei Liu, Jiaxiang Wu +6
Post-training quantization (PTQ) is a popular method for compressing deep neural networks (DNNs) without modifying their original architecture or training procedures. Despite its e…
MobileInst: Video Instance Segmentation on the Mobile
Renhong Zhang, Tianheng Cheng, Shusheng Yang +8
Video instance segmentation on mobile devices is an important yet very challenging edge AI problem. It mainly suffers from (1) heavy computation and memory costs for frame-by-frame…
VAD: Vectorized Scene Representation for Efficient Autonomous Driving
Bo Jiang, Shaoyu Chen, Qing Xu +7
Autonomous driving requires a comprehensive understanding of the surrounding environment for reliable trajectory planning. Previous works rely on dense rasterized scene representat…
Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction
Bencheng Liao, Shaoyu Chen, Bo Jiang +5
Online lane graph construction is a promising but challenging task in autonomous driving. Previous methods usually model the lane graph at the pixel or piece level, and recover the…
A Simple Adaptive Unfolding Network for Hyperspectral Image Reconstruction
Junyu Wang, Shijie Wang, Wenyu Liu +2
We present a simple, efficient, and scalable unfolding network, SAUNet, to simplify the network design with an adaptive alternate optimization framework for hyperspectral image (HS…
Understanding Self-Supervised Pretraining with Part-Aware Representation Learning
Jie Zhu, Jiyang Qi, Mingyu Ding +6
In this paper, we are interested in understanding self-supervised pretraining through studying the capability that self-supervised representation pretraining methods learn part-awa…
Graph Contrastive Learning for Skeleton-based Action Recognition
Xiaohu Huang, Hao Zhou, Jian Wang +7
In the field of skeleton-based action recognition, current top-performing graph convolutional networks (GCNs) exploit intra-sequence context to construct adaptive graphs for featur…
Perceive, Interact, Predict: Learning Dynamic and Static Clues for End-to-End Motion Prediction
Bo Jiang, Shaoyu Chen, Xinggang Wang +7
Motion prediction is highly relevant to the perception of dynamic objects and static map elements in the scenarios of autonomous driving. In this work, we propose PIP, the first en…
PD-Quant: Post-Training Quantization based on Prediction Difference Metric
Jiawei Liu, Lin Niu, Zhihang Yuan +3
Post-training quantization (PTQ) is a neural network compression technique that converts a full-precision model into a quantized model using lower-precision data types. Although it…
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation
Tianheng Cheng, Xinggang Wang, Shaoyu Chen +2
Labeling objects with pixel-wise segmentation requires a huge amount of human labor compared to bounding boxes. Most existing methods for weakly supervised instance segmentation fo…
Robust Multi-Object Tracking by Marginal Inference
Yifu Zhang, Chunyu Wang, Xinggang Wang +2
Multi-object tracking in videos requires to solve a fundamental problem of one-to-one assignment between objects in adjacent frames. Most methods address the problem by first disca…