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20152023
most citedConditional Random Fields as Recurrent Neural Networks

2.4k citations · 2.5k across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2023★ 20 cited

Sparse4D v2: Recurrent Temporal Fusion with Sparse Model

Xuewu Lin, Tianwei Lin, Zixiang Pei +2

Sparse algorithms offer great flexibility for multi-view temporal perception tasks. In this paper, we present an enhanced version of Sparse4D, in which we improve the temporal fusi…

cs.CV2022★ 30 cited

Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal Fusion

Xuewu Lin, Tianwei Lin, Zixiang Pei +2

Bird-eye-view (BEV) based methods have made great progress recently in multi-view 3D detection task. Comparing with BEV based methods, sparse based methods lag behind in performanc…

cs.CV2021

HybridGazeNet: Geometric model guided Convolutional Neural Networks for gaze estimation

Shaobo Guo, Xiao Jiang, Zhizhong Su +2

As a critical cue for understanding human intention, human gaze provides a key signal for Human-Computer Interaction(HCI) applications. Appearance-based gaze estimation, which dire…

cs.CV2020★ 2 cited

Gaussian Vector: An Efficient Solution for Facial Landmark Detection

Yilin Xiong, Zijian Zhou, Yuhao Dou +1

Significant progress has been made in facial landmark detection with the development of Convolutional Neural Networks. The widely-used algorithms can be classified into coordinate…

cs.CV2019★ 6 cited

VarGFaceNet: An Efficient Variable Group Convolutional Neural Network for Lightweight Face Recognition

Mengjia Yan, Mengao Zhao, Zining Xu +3

To improve the discriminative and generalization ability of lightweight network for face recognition, we propose an efficient variable group convolutional network called VarGFaceNe…

cs.CV2015★ 2.4k cited

Conditional Random Fields as Recurrent Neural Networks

Shuai Zheng, Sadeep Jayasumana, Bernardino Romera-Paredes +5

Pixel-level labelling tasks, such as semantic segmentation, play a central role in image understanding. Recent approaches have attempted to harness the capabilities of deep learnin…