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20182024
most citedLearnable Tree Filter for Structure-preserving Feature Transform

22 citations · 54 across the 7 of their papers we have counts for

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Showing cs.CVShow all

8 papers · 1 filter

cs.CV2024

Exploring Hardware Friendly Bottleneck Architecture in CNN for Embedded Computing Systems

Xing Lei, Longjun Liu, Zhiheng Zhou +2

In this paper, we explore how to design lightweight CNN architecture for embedded computing systems. We propose L-Mobilenet model for ZYNQ based hardware platform. L-Mobilenet can…

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.CV20201 cited

ADCPNet: Adaptive Disparity Candidates Prediction Network for Efficient Real-Time Stereo Matching

He Dai, Xuchong Zhang, Yongli Zhao +1

Efficient real-time disparity estimation is critical for the application of stereo vision systems in various areas. Recently, stereo network based on coarse-to-fine method has larg…

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…