Two-branch Objectness-centric Open World Detection-Reference-Cited by-同舟云学术

Two-branch Objectness-centric Open World Detection

Published:2022-10-10 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 3rd International Workshop on Human-Centric Multimedia Analysis
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Author:

Wu Yan¹,Zhao Xiaowei¹,Ma Yuqing¹,Wang Duorui¹,Liu Xianglong¹

Affiliation:

1. State Key Lab of Software Development Environment, Beihang University, Beijing, China

Funder

Beijing Nova Program of Science and Technology

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3552458.3556453

Reference23 articles.

1. Towards Open World Recognition

2. Towards Open Set Deep Networks

3. D2Det: Towards High Quality Object Detection and Instance Segmentation

4. The Overlooked Elephant of Object Detection: Open Set

5. Akshay Raj Dhamija , Manuel Günther , and Terrance E Boult . 2018 . Reducing network agnostophobia . In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 9175--9186 . Akshay Raj Dhamija, Manuel Günther, and Terrance E Boult. 2018. Reducing network agnostophobia. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 9175--9186.

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1. Open-Set Object Detection By Aligning Known Class Representations;2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV);2024-01-03

2. CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06

3. PROB: Probabilistic Objectness for Open World Object Detection;2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2023-06