67 citations · 127 across the 6 of their papers we have counts for
6 papers
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…
Distribution Alignment: A Unified Framework for Long-tail Visual Recognition
Songyang Zhang, Zeming Li, Shipeng Yan +2
Despite the recent success of deep neural networks, it remains challenging to effectively model the long-tail class distribution in visual recognition tasks. To address this proble…
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…
End-to-End Object Detection with Fully Convolutional Network
Jianfeng Wang, Lin Song, Zeming Li +3
Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS…
Fully Convolutional Networks for Panoptic Segmentation
Yanwei Li, Hengshuang Zhao, Xiaojuan Qi +4
In this paper, we present a conceptually simple, strong, and efficient framework for panoptic segmentation, called Panoptic FCN. Our approach aims to represent and predict foregrou…
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…