Výsledky vyhledávání - "Su, Z. (Zhuo)"

SVNet: Where SO(3) Equivariance Meets Binarization on Point Cloud Representation

Autor: Su, Z. (Zhuo), Welling, M. (Max), Pietikainen, M. (Matti), Liu, L. (Li)

Efficiency and robustness are increasingly needed for applications on 3D point clouds, with the ubiquitous use of edge devices in scenarios like autonomous driving and robotics, which often demand real-time and reliable responses. The paper tackles t

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7357fadb517c6d1e5891f77fa1edddb2
http://arxiv.org/abs/2209.05924

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Pixel Difference Networks for Efficient Edge Detection

Autor: Su, Z. (Zhuo), Liu, W. (Wenzhe), Yu, Z. (Zitong), Hu, D. (Dewen), Liao, Q. (Qing), Tian, Q. (Qi), Pietikäinen, M. (Matti), Liu, L. (Li)

Publikováno v: 2021 IEEE/CVF International Conference on Computer Vision (ICCV).

Recently, deep Convolutional Neural Networks (CNNs) can achieve human-level performance in edge detection with the rich and abstract edge representation capacities. However, the high performance of CNN based edge detection is achieved with a large pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bc5430a18e2208572190864a59c4bd0c
https://doi.org/10.1109/iccv48922.2021.00507

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Beyond vanilla convolution:random pixel difference convolution for face perception

Autor: Liu, W. (Wenzhe), Su, Z. (Zhuo), Liu, L. (Li)

Face perception is an essential and significant problem in pattern recognition, concretely including Face Recognition (FR), Facial Expression Recognition (FER), and Race Categorization (RC). Though handcrafted features perform well on face images, De

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2423::40a20cda9259b7ffc32e68ace58c39e0
http://urn.fi/urn:nbn:fi-fe2021111154658

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BIRD:learning binary and illumination robust descriptor for face recognition

Autor: Su, Z. (Zhuo), Pietikäinen, M. (Matti), Liu, L. (Li)

Recently face recognition has made significantly progress due to the advancement of large scale Deep Convolutional Neural Network (DeepCNNs). Despite the great success, the known deficiencies of DeepCNNs have not been addressed, such as the need for

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2423::dbbb2ea211393dce0328644c6be1818a
http://urn.fi/urn:nbn:fi-fe202001131871

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