Search Results for author: Quanquan Li

Found 22 papers, 7 papers with code

RefineMask: Towards High-Quality Instance Segmentation with Fine-Grained Features

1 code implementation CVPR 2021 Gang Zhang, Xin Lu, Jingru Tan, Jianmin Li, Zhaoxiang Zhang, Quanquan Li, Xiaolin Hu

In this work, we propose a new method called RefineMask for high-quality instance segmentation of objects and scenes, which incorporates fine-grained features during the instance-wise segmenting process in a multi-stage manner.

Instance Segmentation Semantic Segmentation

Fixing the Teacher-Student Knowledge Discrepancy in Distillation

no code implementations31 Mar 2021 Jiangfan Han, Mengya Gao, Yujie Wang, Quanquan Li, Hongsheng Li, Xiaogang Wang

To solve this problem, in this paper, we propose a novel student-dependent distillation method, knowledge consistent distillation, which makes teacher's knowledge more consistent with the student and provides the best suitable knowledge to different student networks for distillation.

Knowledge Distillation Object Detection

Differentiable Network Adaption with Elastic Search Space

no code implementations30 Mar 2021 Shaopeng Guo, Yujie Wang, Kun Yuan, Quanquan Li

In this paper we propose a novel network adaption method called Differentiable Network Adaption (DNA), which can adapt an existing network to a specific computation budget by adjusting the width and depth in a differentiable manner.

Neural Architecture Search

Differentiable Dynamic Wirings for Neural Networks

no code implementations ICCV 2021 Kun Yuan, Quanquan Li, Shaopeng Guo, Dapeng Chen, Aojun Zhou, Fengwei Yu, Ziwei Liu

A standard practice of deploying deep neural networks is to apply the same architecture to all the input instances.

Object Detection

Multi-scale Network Architecture Search for Object Detection

no code implementations1 Jan 2021 Yuxin Yue, Quanquan Li, Yujie Wang

Many commonly-used detection frameworks aim to handle the multi-scale object detection problem.

Object Detection

Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection

2 code implementations CVPR 2021 Jingru Tan, Xin Lu, Gang Zhang, Changqing Yin, Quanquan Li

To address the problem of imbalanced gradients, we introduce a new version of equalization loss, called equalization loss v2 (EQL v2), a novel gradient guided reweighing mechanism that re-balances the training process for each category independently and equally.

Instance Segmentation Object Detection

Dynamic Graph: Learning Instance-aware Connectivity for Neural Networks

no code implementations2 Oct 2020 Kun Yuan, Quanquan Li, Dapeng Chen, Aojun Zhou, Junjie Yan

To facilitate the training, we represent the network connectivity of each sample in an adjacency matrix.

Graph Learning Object Detection

MimicDet: Bridging the Gap Between One-Stage and Two-Stage Object Detection

no code implementations ECCV 2020 Xin Lu, Quanquan Li, Buyu Li, Junjie Yan

In this paper, we propose MimicDet, a novel and efficient framework to train a one-stage detector by directly mimic the two-stage features, aiming to bridge the accuracy gap between one-stage and two-stage detectors.

Object Detection

DMCP: Differentiable Markov Channel Pruning for Neural Networks

1 code implementation CVPR 2020 Shaopeng Guo, Yujie Wang, Quanquan Li, Junjie Yan

In DMCP, we model the channel pruning as a Markov process, in which each state represents for retaining the corresponding channel during pruning, and transitions between states denote the pruning process.

Equalization Loss for Long-Tailed Object Recognition

1 code implementation CVPR 2020 Jingru Tan, Changbao Wang, Buyu Li, Quanquan Li, Wanli Ouyang, Changqing Yin, Junjie Yan

Based on it, we propose a simple but effective loss, named equalization loss, to tackle the problem of long-tailed rare categories by simply ignoring those gradients for rare categories.

Object Detection Object Recognition

Residual Knowledge Distillation

no code implementations21 Feb 2020 Mengya Gao, Yujun Shen, Quanquan Li, Chen Change Loy

Knowledge distillation (KD) is one of the most potent ways for model compression.

Knowledge Distillation Model Compression

Equalization Loss for Large Vocabulary Instance Segmentation

no code implementations12 Nov 2019 Jingru Tan, Changbao Wang, Quanquan Li, Junjie Yan

Recent object detection and instance segmentation tasks mainly focus on datasets with a relatively small set of categories, e. g. Pascal VOC with 20 classes and COCO with 80 classes.

Instance Segmentation Object Detection +1

Diving into Optimization of Topology in Neural Networks

no code implementations25 Sep 2019 Kun Yuan, Quanquan Li, Yucong Zhou, Jing Shao, Junjie Yan

Seeking effective networks has become one of the most crucial and practical areas in deep learning.

Face Recognition Image Classification +1

Grid R-CNN Plus: Faster and Better

2 code implementations13 Jun 2019 Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan

Grid R-CNN is a well-performed objection detection framework.

Object Detection

An Embarrassingly Simple Approach for Knowledge Distillation

1 code implementation5 Dec 2018 Mengya Gao, Yujun Shen, Quanquan Li, Junjie Yan, Liang Wan, Dahua Lin, Chen Change Loy, Xiaoou Tang

Knowledge Distillation (KD) aims at improving the performance of a low-capacity student model by inheriting knowledge from a high-capacity teacher model.

Face Recognition Knowledge Distillation +2

Grid R-CNN

2 code implementations CVPR 2019 Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, Junjie Yan

This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection.

Object Detection Object Localization

Mimicking Very Efficient Network for Object Detection

no code implementations CVPR 2017 Quanquan Li, Shengying Jin, Junjie Yan

More specifically, we conduct a mimic method for the features sampled from the entire feature map and use a transform layer to map features from the small network onto the same dimension of the large network.

Object Detection

POI: Multiple Object Tracking with High Performance Detection and Appearance Feature

no code implementations19 Oct 2016 Fengwei Yu, Wenbo Li, Quanquan Li, Yu Liu, Xiaohua Shi, Junjie Yan

In this paper, we explore the high-performance detection and deep learning based appearance feature, and show that they lead to significantly better MOT results in both online and offline setting.

Multiple Object Tracking

Cannot find the paper you are looking for? You can Submit a new open access paper.