Search Results for author: Songtao Liu

Found 14 papers, 9 papers with code

YOLOX: Exceeding YOLO Series in 2021

1 code implementation18 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

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

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection

1 code implementation12 Jan 2021 Zheng Ge, JianFeng Wang, Xin Huang, Songtao Liu, Osamu Yoshie

A joint loss is then defined as the weighted summation of cls and reg losses as the assigning indicator.

Object Detection Pedestrian Detection

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

BorderDet: Border Feature for Dense Object Detection

1 code implementation 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

Multi-Scale Positive Sample Refinement for Few-Shot Object Detection

1 code implementation ECCV 2020 Jiaxi Wu, Songtao Liu, Di Huang, Yunhong Wang

Few-shot object detection (FSOD) helps detectors adapt to unseen classes with few training instances, and is useful when manual annotation is time-consuming or data acquisition is limited.

Few-Shot Object Detection

AutoAssign: Differentiable Label Assignment for Dense Object Detection

1 code implementation7 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

Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation

no code implementations CVPR 2020 Yangtao Zheng, Di Huang, Songtao Liu, Yunhong Wang

Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred.

Object Detection

Learning Spatial Fusion for Single-Shot Object Detection

1 code implementation21 Nov 2019 Songtao Liu, Di Huang, Yunhong Wang

Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection.

Object Detection

Higher-order Weighted Graph Convolutional Networks

no code implementations11 Nov 2019 Songtao Liu, Lingwei Chen, Hanze Dong, ZiHao Wang, Dinghao Wu, Zengfeng Huang

Graph Convolution Network (GCN) has been recognized as one of the most effective graph models for semi-supervised learning, but it extracts merely the first-order or few-order neighborhood information through information propagation, which suffers performance drop-off for deeper structure.

Node Classification

Receptive Field Block Net for Accurate and Fast Object Detection

7 code implementations ECCV 2018 Songtao Liu, Di Huang, Yunhong Wang

Current top-performing object detectors depend on deep CNN backbones, such as ResNet-101 and Inception, benefiting from their powerful feature representations but suffering from high computational costs.

Real-Time Object Detection

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