activity
20192021
most citedLearnable Tree Filter for Structure-preserving Feature Transform

22 citations · 60 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV20217 cited

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…

cs.CV20203 cited

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…

cs.CV20206 cited

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…

cs.CV202014 cited

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…

cs.CV2020

Learning Dynamic Routing for Semantic Segmentation

Yanwei Li, Lin Song, Yukang Chen +4

Recently, numerous handcrafted and searched networks have been applied for semantic segmentation. However, previous works intend to handle inputs with various scales in pre-defined…

cs.CV201922 cited

Learnable Tree Filter for Structure-preserving Feature Transform

Lin Song, Yanwei Li, Zeming Li +4

Learning discriminative global features plays a vital role in semantic segmentation. And most of the existing methods adopt stacks of local convolutions or non-local blocks to capt…