3k citations · 3.5k across the 20 of their papers we have counts for
10 papers · 1 filter
Fine-Grained Dynamic Head for Object Detection
Lin Song, Yanwei Li, Zhengkai Jiang +4
The Feature Pyramid Network (FPN) presents a remarkable approach to alleviate the scale variance in object representation by performing instance-level assignments. Nevertheless, th…
Rethinking Learnable Tree Filter for Generic Feature Transform
Lin Song, Yanwei Li, Zhengkai Jiang +5
The Learnable Tree Filter presents a remarkable approach to model structure-preserving relations for semantic segmentation. Nevertheless, the intrinsic geometric constraint forces…
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…
Joint COCO and Mapillary Workshop at ICCV 2019: COCO Instance Segmentation Challenge Track
Zeming Li, Yuchen Ma, Yukang Chen +2
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. Ou…