Search Results for author: Li Jiang

Found 47 papers, 28 papers with code

Offline Reinforcement Learning with Imbalanced Datasets

no code implementations6 Jul 2023 Li Jiang, Sijie Chen, JieLin Qiu, Haoran Xu, Wai Kin Chan, Zhao Ding

The prevalent use of benchmarks in current offline reinforcement learning (RL) research has led to a neglect of the imbalance of real-world dataset distributions in the development of models.

D4RL Offline RL +4

MTR++: Multi-Agent Motion Prediction with Symmetric Scene Modeling and Guided Intention Querying

1 code implementation30 Jun 2023 Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele

Extensive experimental results demonstrate that the MTR framework achieves state-of-the-art performance on the highly-competitive motion prediction benchmarks, while the MTR++ framework surpasses its precursor, exhibiting enhanced performance and efficiency in predicting accurate multimodal future trajectories for multiple agents.

Autonomous Driving motion prediction

FreePoint: Unsupervised Point Cloud Instance Segmentation

no code implementations11 May 2023 Zhikai Zhang, Jian Ding, Li Jiang, Dengxin Dai, Gui-Song Xia

Based on the point features, we perform a multicut algorithm to segment point clouds into coarse instance masks as pseudo labels, which are used to train a point cloud instance segmentation model.

Instance Segmentation Segmentation +2

A Deep Learning Framework for Traffic Data Imputation Considering Spatiotemporal Dependencies

no code implementations18 Apr 2023 Li Jiang, Ting Zhang, Qiruyi Zuo, Chenyu Tian, George P. Chan, Wai Kin, Chan

Spatiotemporal (ST) data collected by sensors can be represented as multi-variate time series, which is a sequence of data points listed in an order of time.

Imputation Time Series +1

Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization

2 code implementations28 Mar 2023 Haoran Xu, Li Jiang, Jianxiong Li, Zhuoran Yang, Zhaoran Wang, Victor Wai Kin Chan, Xianyuan Zhan

This gives a deeper understanding of why the in-sample learning paradigm works, i. e., it applies implicit value regularization to the policy.

D4RL Offline RL +2

Learning Context-aware Classifier for Semantic Segmentation

2 code implementations21 Mar 2023 Zhuotao Tian, Jiequan Cui, Li Jiang, Xiaojuan Qi, Xin Lai, Yixin Chen, Shu Liu, Jiaya Jia

Semantic segmentation is still a challenging task for parsing diverse contexts in different scenes, thus the fixed classifier might not be able to well address varying feature distributions during testing.

Segmentation Semantic Segmentation

A Policy-Guided Imitation Approach for Offline Reinforcement Learning

1 code implementation15 Oct 2022 Haoran Xu, Li Jiang, Jianxiong Li, Xianyuan Zhan

We decompose the conventional reward-maximizing policy in offline RL into a guide-policy and an execute-policy.

D4RL Offline RL +3

Point Transformer V2: Grouped Vector Attention and Partition-based Pooling

2 code implementations11 Oct 2022 Xiaoyang Wu, Yixing Lao, Li Jiang, Xihui Liu, Hengshuang Zhao

In this work, we analyze the limitations of the Point Transformer and propose our powerful and efficient Point Transformer V2 model with novel designs that overcome the limitations of previous work.

3D Point Cloud Classification 3D Semantic Segmentation +4

Motion Transformer with Global Intention Localization and Local Movement Refinement

2 code implementations27 Sep 2022 Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele

Predicting multimodal future behavior of traffic participants is essential for robotic vehicles to make safe decisions.

motion prediction Trajectory Prediction

MTR-A: 1st Place Solution for 2022 Waymo Open Dataset Challenge -- Motion Prediction

1 code implementation20 Sep 2022 Shaoshuai Shi, Li Jiang, Dengxin Dai, Bernt Schiele

In this report, we present the 1st place solution for motion prediction track in 2022 Waymo Open Dataset Challenges.

motion prediction

SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image

1 code implementation28 Jul 2022 Yan Hu, Zhongxi Qiu, Dan Zeng, Li Jiang, Chen Lin, Jiang Liu

Vascular segmentation extracts blood vessels from images and serves as the basis for diagnosing various diseases, like ophthalmic diseases.

Medical Image Segmentation Segmentation +1

Boosting Single-Frame 3D Object Detection by Simulating Multi-Frame Point Clouds

no code implementations3 Jul 2022 Wu Zheng, Li Jiang, Fanbin Lu, Yangyang Ye, Chi-Wing Fu

To boost a detector for single-frame 3D object detection, we present a new approach to train it to simulate features and responses following a detector trained on multi-frame point clouds.

3D Object Detection object-detection +1

Boosting 3D Object Detection by Simulating Multimodality on Point Clouds

no code implementations CVPR 2022 Wu Zheng, Mingxuan Hong, Li Jiang, Chi-Wing Fu

This paper presents a new approach to boost a single-modality (LiDAR) 3D object detector by teaching it to simulate features and responses that follow a multi-modality (LiDAR-image) detector.

3D Object Detection object-detection

Stratified Transformer for 3D Point Cloud Segmentation

4 code implementations CVPR 2022 Xin Lai, Jianhui Liu, Li Jiang, LiWei Wang, Hengshuang Zhao, Shu Liu, Xiaojuan Qi, Jiaya Jia

In this paper, we propose Stratified Transformer that is able to capture long-range contexts and demonstrates strong generalization ability and high performance.

Point Cloud Segmentation Semantic Segmentation

DNN Training Acceleration via Exploring GPGPU Friendly Sparsity

no code implementations11 Mar 2022 Zhuoran Song, Yihong Xu, Han Li, Naifeng Jing, Xiaoyao Liang, Li Jiang

The training phases of Deep neural network~(DNN) consumes enormous processing time and energy.

CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction

1 code implementation9 Mar 2022 Zhuoran Song, Yihong Xu, Zhezhi He, Li Jiang, Naifeng Jing, Xiaoyao Liang

We explore the sparsity in ViT and observe that informative patches and heads are sufficient for accurate image recognition.

A Unified Query-based Paradigm for Point Cloud Understanding

1 code implementation CVPR 2022 Zetong Yang, Li Jiang, Yanan sun, Bernt Schiele, Jiaya Jia

This is achieved by introducing an intermediate representation, i. e., Q-representation, in the querying stage to serve as a bridge between the embedding stage and task heads.

Autonomous Driving object-detection +2

Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals

no code implementations30 Jan 2022 Weidong Cao, Yilong Zhao, Adith Boloor, Yinhe Han, Xuan Zhang, Li Jiang

This paper presents a new PIM architecture to efficiently accelerate deep learning tasks by minimizing the required A/D conversions with analog accumulation and neural approximated peripheral circuits.


N3H-Core: Neuron-designed Neural Network Accelerator via FPGA-based Heterogeneous Computing Cores

1 code implementation15 Dec 2021 Yu Gong, Zhihan Xu, Zhezhi He, Weifeng Zhang, Xiaobing Tu, Xiaoyao Liang, Li Jiang

From the software perspective, we mathematically and systematically model the latency and resource utilization of the proposed heterogeneous accelerator, regarding varying system design configurations.


Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation

1 code implementation ICCV 2021 Li Jiang, Shaoshuai Shi, Zhuotao Tian, Xin Lai, Shu Liu, Chi-Wing Fu, Jiaya Jia

To address the high cost and challenges of 3D point-level labeling, we present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance.

3D Semantic Segmentation Contrastive Learning +1

AppealNet: An Efficient and Highly-Accurate Edge/Cloud Collaborative Architecture for DNN Inference

no code implementations10 May 2021 Min Li, Yu Li, Ye Tian, Li Jiang, Qiang Xu

This paper presents AppealNet, a novel edge/cloud collaborative architecture that runs deep learning (DL) tasks more efficiently than state-of-the-art solutions.

Image Classification

Skimming and Scanning for Untrimmed Video Action Recognition

no code implementations21 Apr 2021 Yunyan Hong, Ailing Zeng, Min Li, Cewu Lu, Li Jiang, Qiang Xu

Video action recognition (VAR) is a primary task of video understanding, and untrimmed videos are more common in real-life scenes.

Action Recognition Temporal Action Localization +1

SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud

1 code implementation CVPR 2021 Wu Zheng, Weiliang Tang, Li Jiang, Chi-Wing Fu

Lastly, to better exploit hard targets, we design an ODIoU loss to supervise the student with constraints on the predicted box centers and orientations.

3D Object Detection Birds Eye View Object Detection +1

Bidirectional Projection Network for Cross Dimension Scene Understanding

1 code implementation CVPR 2021 WenBo Hu, Hengshuang Zhao, Li Jiang, Jiaya Jia, Tien-Tsin Wong

Via the \emph{BPM}, complementary 2D and 3D information can interact with each other in multiple architectural levels, such that advantages in these two visual domains can be combined for better scene recognition.

2D Semantic Segmentation 3D Semantic Segmentation +3

Detecting anomalous quartic gauge couplings using the isolation forest machine learning algorithm

no code implementations4 Mar 2021 Yu-Chen Guo, Li Jiang, Ji-Chong Yang

The search of new physics~(NP) beyond the Standard Model is one of the most important tasks of high energy physics.

High Energy Physics - Phenomenology

Point Transformer

22 code implementations ICCV 2021 Hengshuang Zhao, Li Jiang, Jiaya Jia, Philip Torr, Vladlen Koltun

For example, on the challenging S3DIS dataset for large-scale semantic scene segmentation, the Point Transformer attains an mIoU of 70. 4% on Area 5, outperforming the strongest prior model by 3. 3 absolute percentage points and crossing the 70% mIoU threshold for the first time.

3D Part Segmentation 3D Point Cloud Classification +7

CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud

1 code implementation5 Dec 2020 Wu Zheng, Weiliang Tang, Sijin Chen, Li Jiang, Chi-Wing Fu

Existing single-stage detectors for locating objects in point clouds often treat object localization and category classification as separate tasks, so the localization accuracy and classification confidence may not well align.

3D Object Detection Birds Eye View Object Detection +3

Generalized Few-shot Semantic Segmentation

1 code implementation CVPR 2022 Zhuotao Tian, Xin Lai, Li Jiang, Shu Liu, Michelle Shu, Hengshuang Zhao, Jiaya Jia

Then, since context is essential for semantic segmentation, we propose the Context-Aware Prototype Learning (CAPL) that significantly improves performance by 1) leveraging the co-occurrence prior knowledge from support samples, and 2) dynamically enriching contextual information to the classifier, conditioned on the content of each query image.

Generalized Few-Shot Semantic Segmentation Segmentation +1

PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection

9 code implementations CVPR 2020 Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang, Jianping Shi, Xiaogang Wang, Hongsheng Li

We present a novel and high-performance 3D object detection framework, named PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.

object-detection Robust 3D Object Detection

Measurement Scheduling for Cooperative Localization in Resource-Constrained Conditions

1 code implementation10 Dec 2019 Qi Yan, Li Jiang, Solmaz Kia

Optimal selection of which teammates a robot should take a relative measurement from such that the updated joint localization uncertainty of the team is minimized is an NP-hard problem.


Hierarchical Point-Edge Interaction Network for Point Cloud Semantic Segmentation

no code implementations ICCV 2019 Li Jiang, Hengshuang Zhao, Shu Liu, Xiaoyong Shen, Chi-Wing Fu, Jiaya Jia

To incorporate point features in the edge branch, we establish a hierarchical graph framework, where the graph is initialized from a coarse layer and gradually enriched along the point decoding process.

Scene Labeling Semantic Segmentation

An Ultra-Efficient Memristor-Based DNN Framework with Structured Weight Pruning and Quantization Using ADMM

no code implementations29 Aug 2019 Geng Yuan, Xiaolong Ma, Caiwen Ding, Sheng Lin, Tianyun Zhang, Zeinab S. Jalali, Yilong Zhao, Li Jiang, Sucheta Soundarajan, Yanzhi Wang

Memristor-based weight pruning and weight quantization have been seperately investigated and proven effectiveness in reducing area and power consumption compared to the original DNN model.


Invocation-driven Neural Approximate Computing with a Multiclass-Classifier and Multiple Approximators

1 code implementation19 Oct 2018 Haiyue Song, Chengwen Xu, Qiang Xu, Zhuoran Song, Naifeng Jing, Xiaoyao Liang, Li Jiang

We thus propose a novel approximate computing architecture with a Multiclass-Classifier and Multiple Approximators (MCMA).

AXNet: ApproXimate computing using an end-to-end trainable neural network

2 code implementations27 Jul 2018 Zhenghao Peng, Xuyang Chen, Chengwen Xu, Naifeng Jing, Xiaoyao Liang, Cewu Lu, Li Jiang

To guarantee the approximation quality, existing works deploy two neural networks (NNs), e. g., an approximator and a predictor.

Multi-Task Learning Philosophy

Approximate Random Dropout

no code implementations23 May 2018 Zhuoran Song, Ru Wang, Dongyu Ru, Hongru Huang, Zhenghao Peng, Jing Ke, Xiaoyao Liang, Li Jiang

In this paper, we propose the Approximate Random Dropout that replaces the conventional random dropout of neurons and synapses with a regular and predefined patterns to eliminate the unnecessary computation and data access.

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