3k citations · 4.1k across the 23 of their papers we have counts for
23 papers
Align-DETR: Enhancing End-to-end Object Detection with Aligned Loss
Zhi Cai, Songtao Liu, Guodong Wang +3
DETR has set up a simple end-to-end pipeline for object detection by formulating this task as a set prediction problem, showing promising potential. Despite its notable advancement…
Dynamic Grained Encoder for Vision Transformers
Lin Song, Songyang Zhang, Songtao Liu +5
Transformers, the de-facto standard for language modeling, have been recently applied for vision tasks. This paper introduces sparse queries for vision transformers to exploit the…
Generalizing Multiple Object Tracking to Unseen Domains by Introducing Natural Language Representation
En Yu, Songtao Liu, Zhuoling Li +4
Although existing multi-object tracking (MOT) algorithms have obtained competitive performance on various benchmarks, almost all of them train and validate models on the same domai…
StreamYOLO: Real-time Object Detection for Streaming Perception
Jinrong Yang, Songtao Liu, Zeming Li +2
The perceptive models of autonomous driving require fast inference within a low latency for safety. While existing works ignore the inevitable environmental changes after processin…
Dense Teacher: Dense Pseudo-Labels for Semi-supervised Object Detection
Hongyu Zhou, Zheng Ge, Songtao Liu +4
To date, the most powerful semi-supervised object detectors (SS-OD) are based on pseudo-boxes, which need a sequence of post-processing with fine-tuned hyper-parameters. In this wo…
DBQ-SSD: Dynamic Ball Query for Efficient 3D Object Detection
Jinrong Yang, Lin Song, Songtao Liu +6
Many point-based 3D detectors adopt point-feature sampling strategies to drop some points for efficient inference. These strategies are typically based on fixed and handcrafted rul…
Real-time Object Detection for Streaming Perception
Jinrong Yang, Songtao Liu, Zeming Li +2
Autonomous driving requires the model to perceive the environment and (re)act within a low latency for safety. While past works ignore the inevitable changes in the environment aft…
Local Augmentation for Graph Neural Networks
Songtao Liu, Rex Ying, Hanze Dong +6
Graph Neural Networks (GNNs) have achieved remarkable performance on graph-based tasks. The key idea for GNNs is to obtain informative representation through aggregating informatio…
YOLOX: Exceeding YOLO Series in 2021
Zheng Ge, Songtao Liu, Feng Wang +2
In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and…
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge
Songyang Zhang, Lin Song, Songtao Liu +4
In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario. Our detector is built on a newly designed YOLO model, called YO…
IQDet: Instance-wise Quality Distribution Sampling for Object Detection
Yuchen Ma, Songtao Liu, Zeming Li +1
We propose a dense object detector with an instance-wise sampling strategy, named IQDet. Instead of using human prior sampling strategies, we first extract the regional feature of…
OTA: Optimal Transport Assignment for Object Detection
Zheng Ge, Songtao Liu, Zeming Li +2
Recent advances in label assignment in object detection mainly seek to independently define positive/negative training samples for each ground-truth (gt) object. In this paper, we…
Momentum^2 Teacher: Momentum Teacher with Momentum Statistics for Self-Supervised Learning
Zeming Li, Songtao Liu, Jian Sun
In this paper, we present a novel approach, Momentum Teacher, for student-teacher based self-supervised learning. The approach performs momentum update on both network weights…
LLA: Loss-aware Label Assignment for Dense Pedestrian Detection
Zheng Ge, Jianfeng Wang, Xin Huang +2
Label assignment has been widely studied in general object detection because of its great impact on detectors' performance. However, none of these works focus on label assignment i…
Self-EMD: Self-Supervised Object Detection without ImageNet
Songtao Liu, Zeming Li, Jian Sun
In this paper, we propose a novel self-supervised representation learning method, Self-EMD, for object detection. Our method directly trained on unlabeled non-iconic image dataset…
Multi-Scale Positive Sample Refinement for Few-Shot Object Detection
Jiaxi Wu, Songtao Liu, Di Huang +1
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…
BorderDet: Border Feature for Dense Object Detection
Han Qiu, Yuchen Ma, Zeming Li +2
Dense object detectors rely on the sliding-window paradigm that predicts the object over a regular grid of image. Meanwhile, the feature maps on the point of the grid are adopted t…
AutoAssign: Differentiable Label Assignment for Dense Object Detection
Benjin Zhu, Jianfeng Wang, Zhengkai Jiang +4
Determining positive/negative samples for object detection is known as label assignment. Here we present an anchor-free detector named AutoAssign. It requires little human knowledg…
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation
Yangtao Zheng, Di Huang, Songtao Liu +1
Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain l…
Learning Spatial Fusion for Single-Shot Object Detection
Songtao Liu, Di Huang, Yunhong Wang
Pyramidal feature representation is the common practice to address the challenge of scale variation in object detection. However, the inconsistency across different feature scales…
Higher-order Weighted Graph Convolutional Networks
Songtao Liu, Lingwei Chen, Hanze Dong +3
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 neig…
Adaptive NMS: Refining Pedestrian Detection in a Crowd
Songtao Liu, Di Huang, Yunhong Wang
Pedestrian detection in a crowd is a very challenging issue. This paper addresses this problem by a novel Non-Maximum Suppression (NMS) algorithm to better refine the bounding boxe…
Receptive Field Block Net for Accurate and Fast Object Detection
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 hi…