Search Results for author: Zeming Li

Found 30 papers, 23 papers with code

Real-time Object Detection for Streaming Perception

1 code implementation23 Mar 2022 Jinrong Yang, Songtao Liu, Zeming Li, Xiaoping Li, Jian Sun

In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem.

 Ranked #1 on Real-Time Object Detection on Argoverse-HD (Full-Stack, Val) (sAP metric, using extra training data)

Autonomous Driving Real-Time Object Detection

Dynamic Grained Encoder for Vision Transformers

1 code implementation NeurIPS 2021 Lin Song, Songyang Zhang, Songtao Liu, Zeming Li, Xuming He, Hongbin Sun, Jian Sun, Nanning Zheng

Specifically, we propose a Dynamic Grained Encoder for vision transformers, which can adaptively assign a suitable number of queries to each spatial region.

Image Classification Language Modelling +1

Fully Convolutional Networks for Panoptic Segmentation with Point-based Supervision

1 code implementation17 Aug 2021 Yanwei Li, Hengshuang Zhao, Xiaojuan Qi, Yukang Chen, Lu Qi, LiWei Wang, Zeming Li, Jian Sun, Jiaya Jia

In particular, Panoptic FCN encodes each object instance or stuff category with the proposed kernel generator and produces the prediction by convolving the high-resolution feature directly.

Panoptic Segmentation Weakly-supervised panoptic segmentation

YOLOX: Exceeding YOLO Series in 2021

24 code implementations18 Jul 2021 Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, Jian Sun

In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.

Autonomous Driving Real-Time Object Detection

OTA: Optimal Transport Assignment for Object Detection

1 code implementation CVPR 2021 Zheng Ge, Songtao Liu, Zeming Li, Osamu Yoshie, Jian Sun

Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object.

Object Detection

Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning

1 code implementation19 Jan 2021 Zeming Li, Songtao Liu, Jian Sun

The teacher's weight is a momentum update of the student, and the teacher's BN statistics is a momentum update of those in history.

Self-Supervised Learning

Fine-Grained Dynamic Head for Object Detection

1 code implementation NeurIPS 2020 Lin Song, Yanwei Li, Zhengkai Jiang, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng

To this end, we propose a fine-grained dynamic head to conditionally select a pixel-level combination of FPN features from different scales for each instance, which further releases the ability of multi-scale feature representation.

Object Detection

End-to-End Object Detection with Fully Convolutional Network

1 code implementation CVPR 2021 JianFeng Wang, Lin Song, Zeming Li, Hongbin Sun, Jian Sun, Nanning Zheng

Mainstream object detectors based on the fully convolutional network has achieved impressive performance.

Object Detection

Fully Convolutional Networks for Panoptic Segmentation

5 code implementations CVPR 2021 Yanwei Li, Hengshuang Zhao, Xiaojuan Qi, LiWei Wang, Zeming Li, Jian Sun, Jiaya Jia

In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN.

 Ranked #1 on Panoptic Segmentation on Cityscapes val (PQst metric)

Panoptic Segmentation

Self-EMD: Self-Supervised Object Detection without ImageNet

no code implementations27 Nov 2020 Songtao Liu, Zeming Li, Jian Sun

Our Faster R-CNN (ResNet50-FPN) baseline achieves 39. 8% mAP on COCO, which is on par with the state of the art self-supervised methods pre-trained on ImageNet.

Object Detection Representation Learning

Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track

no code implementations6 Oct 2020 Zeming Li, Yuchen Ma, Yukang Chen, Xiangyu Zhang, Jian Sun

In this report, we present our object detection/instance segmentation system, MegDetV2, which works in a two-pass fashion, first to detect instances then to obtain segmentation.

Instance Segmentation Object Detection +1

EqCo: Equivalent Rules for Self-supervised Contrastive Learning

1 code implementation5 Oct 2020 Benjin Zhu, Junqiang Huang, Zeming Li, Xiangyu Zhang, Jian Sun

In this paper, we propose a method, named EqCo (Equivalent Rules for Contrastive Learning), to make self-supervised learning irrelevant to the number of negative samples in InfoNCE-based contrastive learning frameworks.

Contrastive Learning Self-Supervised Learning

BorderDet: Border Feature for Dense Object Detection

2 code implementations ECCV 2020 Han Qiu, Yuchen Ma, Zeming Li, Songtao Liu, Jian Sun

In this paper, We propose a simple and efficient operator called Border-Align to extract "border features" from the extreme point of the border to enhance the point feature.

Dense Object Detection

AutoAssign: Differentiable Label Assignment for Dense Object Detection

2 code implementations7 Jul 2020 Benjin Zhu, Jian-Feng Wang, Zhengkai Jiang, Fuhang Zong, Songtao Liu, Zeming Li, Jian Sun

During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions.

Dense Object Detection

Dynamic Scale Training for Object Detection

4 code implementations26 Apr 2020 Yukang Chen, Peizhen Zhang, Zeming Li, Yanwei Li, Xiangyu Zhang, Lu Qi, Jian Sun, Jiaya Jia

We propose a Dynamic Scale Training paradigm (abbreviated as DST) to mitigate scale variation challenge in object detection.

Instance Segmentation Object Detection +1

Learning Dynamic Routing for Semantic Segmentation

no code implementations CVPR 2020 Yanwei Li, Lin Song, Yukang Chen, Zeming Li, Xiangyu Zhang, Xingang Wang, Jian Sun

To demonstrate the superiority of the dynamic property, we compare with several static architectures, which can be modeled as special cases in the routing space.

Semantic Segmentation

Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection

2 code implementations26 Aug 2019 Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou, Zeming Li, Gang Yu

This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019).

3D Object Detection Autonomous Driving

ThunderNet: Towards Real-time Generic Object Detection

4 code implementations28 Mar 2019 Zheng Qin, Zeming Li, Zhaoning Zhang, Yiping Bao, Gang Yu, Yuxing Peng, Jian Sun

In this paper, we investigate the effectiveness of two-stage detectors in real-time generic detection and propose a lightweight two-stage detector named ThunderNet.

Object Detection

DetNet: Design Backbone for Object Detection

no code implementations ECCV 2018 Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun

(1) Recent object detectors like FPN and RetinaNet usually involve extra stages against the task of image classification to handle the objects with various scales.

Classification General Classification +4

DetNet: A Backbone network for Object Detection

2 code implementations17 Apr 2018 Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun

Due to the gap between the image classification and object detection, we propose DetNet in this paper, which is a novel backbone network specifically designed for object detection.

Classification General Classification +4

Light-Head R-CNN: In Defense of Two-Stage Object Detector

5 code implementations20 Nov 2017 Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun

More importantly, simply replacing the backbone with a tiny network (e. g, Xception), our Light-Head R-CNN gets 30. 7 mmAP at 102 FPS on COCO, significantly outperforming the single-stage, fast detectors like YOLO and SSD on both speed and accuracy.

MegDet: A Large Mini-Batch Object Detector

6 code implementations CVPR 2018 Chao Peng, Tete Xiao, Zeming Li, Yuning Jiang, Xiangyu Zhang, Kai Jia, Gang Yu, Jian Sun

The improvements in recent CNN-based object detection works, from R-CNN [11], Fast/Faster R-CNN [10, 31] to recent Mask R-CNN [14] and RetinaNet [24], mainly come from new network, new framework, or novel loss design.

Object Detection

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