Search Results for author: Jianshu Li

Found 24 papers, 9 papers with code

DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving

no code implementations CVPR 2024 Chen Min, Dawei Zhao, Liang Xiao, Jian Zhao, Xinli Xu, Zheng Zhu, Lei Jin, Jianshu Li, Yulan Guo, Junliang Xing, Liping Jing, Yiming Nie, Bin Dai

In this paper, we address this challenge by introducing a world model-based autonomous driving 4D representation learning framework, dubbed \emph{DriveWorld}, which is capable of pre-training from multi-camera driving videos in a spatio-temporal fashion.

3D Object Detection Motion Forecasting +4

Machine Learning Insides OptVerse AI Solver: Design Principles and Applications

no code implementations11 Jan 2024 Xijun Li, Fangzhou Zhu, Hui-Ling Zhen, Weilin Luo, Meng Lu, Yimin Huang, Zhenan Fan, Zirui Zhou, Yufei Kuang, Zhihai Wang, Zijie Geng, Yang Li, Haoyang Liu, Zhiwu An, Muming Yang, Jianshu Li, Jie Wang, Junchi Yan, Defeng Sun, Tao Zhong, Yong Zhang, Jia Zeng, Mingxuan Yuan, Jianye Hao, Jun Yao, Kun Mao

To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.

Decision Making Management

SynSP: Synergy of Smoothness and Precision in Pose Sequences Refinement

1 code implementation CVPR 2024 Tao Wang, Lei Jin, Zheng Wang, Jianshu Li, Liang Li, Fang Zhao, Yu Cheng, Li Yuan, Li Zhou, Junliang Xing, Jian Zhao

To leverage this quality information we propose a motion refinement network termed SynSP to achieve a Synergy of Smoothness and Precision in the sequence refinement tasks.

Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks

1 code implementation24 Oct 2023 Xiaojun Jia, Jianshu Li, Jindong Gu, Yang Bai, Xiaochun Cao

Besides, we provide theoretical analysis to show the model robustness can be improved by the single-step adversarial training with sampled subnetworks.

Point2Mask: Point-supervised Panoptic Segmentation via Optimal Transport

1 code implementation ICCV 2023 Wentong Li, Yuqian Yuan, Song Wang, Jianke Zhu, Jianshu Li, Jian Liu, Lei Zhang

Weakly-supervised image segmentation has recently attracted increasing research attentions, aiming to avoid the expensive pixel-wise labeling.

Image Segmentation Panoptic Segmentation

Multi-Prototype Networks for Unconstrained Set-based Face Recognition

no code implementations13 Feb 2019 Jian Zhao, Jianshu Li, Xiaoguang Tu, Fang Zhao, Yuan Xin, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng

In this paper, we study the challenging unconstrained set-based face recognition problem where each subject face is instantiated by a set of media (images and videos) instead of a single image.

Face Recognition

Learning Generalizable and Identity-Discriminative Representations for Face Anti-Spoofing

1 code implementation17 Jan 2019 Xiaoguang Tu, Jian Zhao, Mei Xie, Guodong Du, Hengsheng Zhang, Jianshu Li, Zheng Ma, Jiashi Feng

Face anti-spoofing (a. k. a presentation attack detection) has drawn growing attention due to the high-security demand in face authentication systems.

Domain Adaptation Face Anti-Spoofing +1

Multi-Fiber Networks for Video Recognition

no code implementations ECCV 2018 Yunpeng Chen, Yannis Kalantidis, Jianshu Li, Shuicheng Yan, Jiashi Feng

In this paper, we aim to reduce the computational cost of spatio-temporal deep neural networks, making them run as fast as their 2D counterparts while preserving state-of-the-art accuracy on video recognition benchmarks.

Ranked #36 on Action Recognition on UCF101 (using extra training data)

Action Classification Action Recognition +1

Object Relation Detection Based on One-shot Learning

no code implementations16 Jul 2018 Li Zhou, Jian Zhao, Jianshu Li, Li Yuan, Jiashi Feng

Detecting the relations among objects, such as "cat on sofa" and "person ride horse", is a crucial task in image understanding, and beneficial to bridging the semantic gap between images and natural language.

Object One-Shot Learning +1

Weakly Supervised Phrase Localization With Multi-Scale Anchored Transformer Network

no code implementations CVPR 2018 Fang Zhao, Jianshu Li, Jian Zhao, Jiashi Feng

In this paper, we propose a novel weakly supervised model, Multi-scale Anchored Transformer Network (MATN), to accurately localize free-form textual phrases with only image-level supervision.

Region Proposal

Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing

2 code implementations10 Apr 2018 Jian Zhao, Jianshu Li, Yu Cheng, Li Zhou, Terence Sim, Shuicheng Yan, Jiashi Feng

Despite the noticeable progress in perceptual tasks like detection, instance segmentation and human parsing, computers still perform unsatisfactorily on visually understanding humans in crowded scenes, such as group behavior analysis, person re-identification and autonomous driving, etc.

Autonomous Driving Clustering +6

Weaving Multi-scale Context for Single Shot Detector

no code implementations8 Dec 2017 Yunpeng Chen, Jianshu Li, Bin Zhou, Jiashi Feng, Shuicheng Yan

For 320x320 input of batch size = 8, WeaveNet reaches 79. 5% mAP on PASCAL VOC 2007 test in 101 fps with only 4 fps extra cost, and further improves to 79. 7% mAP with more iterations.

object-detection Object Detection

Integrated Face Analytics Networks through Cross-Dataset Hybrid Training

no code implementations16 Nov 2017 Jianshu Li, Shengtao Xiao, Fang Zhao, Jian Zhao, Jianan Li, Jiashi Feng, Shuicheng Yan, Terence Sim

Specifically, iFAN achieves an overall F-score of 91. 15% on the Helen dataset for face parsing, a normalized mean error of 5. 81% on the MTFL dataset for facial landmark localization and an accuracy of 45. 73% on the BNU dataset for emotion recognition with a single model.

Face Alignment Face Parsing +1

Multiple-Human Parsing in the Wild

2 code implementations19 May 2017 Jianshu Li, Jian Zhao, Yunchao Wei, Congyan Lang, Yidong Li, Terence Sim, Shuicheng Yan, Jiashi Feng

To address the multi-human parsing problem, we introduce a new multi-human parsing (MHP) dataset and a novel multi-human parsing model named MH-Parser.

Multi-Human Parsing

Multi-stage Object Detection with Group Recursive Learning

no code implementations18 Aug 2016 Jianan Li, Xiaodan Liang, Jianshu Li, Tingfa Xu, Jiashi Feng, Shuicheng Yan

Most of existing detection pipelines treat object proposals independently and predict bounding box locations and classification scores over them separately.

Object object-detection +4

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