Posts by Collection

portfolio

publications

Few-shot Object Detection with Refined Contrastive Learning

Published in International Conference on Tools with Artificial Intelligence (ICTAI), 2023

Few-shot learning, object detection

Recommended citation: Zeyu Shangguan, Huai Lian, Tong Liu, Xingqun Jiang, "Few-shot Object Detection with Refined Contrastive Learning" 2023 IEEE 35th International Conference on Tools with Artificial Intelligence (ICTAI), Atlanta, GA, USA, 2023, pp. 991-996, doi: 10.1109/ICTAI59109.2023.00148.
Download Paper

Identification of Novel Classes for Improving Few-Shot Object Detection

Published in International Conference on Computer Vision Workshops (ICCVW), 2023

Few-shot learning, object detection

Recommended citation: Zeyu Shangguan, Mohammad Rostami, "Identification of Novel Classes for Improving Few-Shot Object Detection" 2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Paris, France, 2023, pp. 3348-3358, doi: 10.1109/ICCVW60793.2023.00360.
Download Paper

Test-Time Linear Out-of-Distribution Detection

Published in Conference on Computer Vision and Pattern Recognition (CVPR), 2024

Out-of-Distribution

Recommended citation: Ke Fan, Tong Liu, Xingyu Qiu, Yikai Wang, Lian Huai, Zeyu Shangguan, Shuang Gou, Fengjian Liu, Yuqian Fu, Yanwei Fu, Xingqun Jiang, "Test-Time Linear Out-of-Distribution Detection." in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 23752-23761, 2024.
Download Paper

Decoupled DETR For Few-shot Object Detection

Published in Asian Conference on Computer Vision (ACCV, oral), 2024

Few-shot object detection

Recommended citation: Zeyu Shangguan, Lian Huai, Tong Liu, Yuyu Liu, Xingqun Jiang (2024). "Decoupled DETR For Few-shot Object Detection." ACCV.
Download Paper

talks

teaching

TA/Grader

AME341aL: Mechoptronics Laboratory I, University of Southern California, Department of Aerospace and Mechanical Engineering, 2019

Instructed students to finish their experiments for the lab course and graded their reports.