W2N:Switching From Weak Supervision to Noisy Supervision for Object
Detection

W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection

25 July 2022

Bowen Dong

Papers citing "W2N:Switching From Weak Supervision to Noisy Supervision for Object Detection"

8 / 8 papers shown

Title
HUWSOD: Holistic Self-training for Unified Weakly Supervised Object Detection Liujuan Cao Jianghang Lin Zebo Hong Yunhang Shen Shaohui Lin Chao Chen Rongrong Ji 26 0 0 27 Jun 2024
WeakSAM: Segment Anything Meets Weakly-supervised Instance-level Recognition Lianghui Zhu Junwei Zhou Yan Liu Xin Hao Wenyu Liu Xinggang Wang VLM 31 5 0 22 Feb 2024
ALWOD: Active Learning for Weakly-Supervised Object Detection Yuting Wang Velibor Ilic Jiatong Li B. Kisačanin Vladimir Pavlovic 14 8 0 14 Sep 2023
Cyclic-Bootstrap Labeling for Weakly Supervised Object Detection Yufei Yin Jiajun Deng Wen-gang Zhou Li Li Houqiang Li 27 3 0 11 Aug 2023
Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection Zhaofei Wang Weijia Zhang Min-Ling Zhang ViT WSOD 15 3 0 27 Mar 2023
D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation Yuting Wang Ricardo Guerrero Vladimir Pavlovic 14 5 0 02 Dec 2022
ImaginaryNet: Learning Object Detectors without Real Images and Annotations Minheng Ni Zitong Huang Kai-Hua Feng W. Zuo VLM 19 15 0 13 Oct 2022
BoxTeacher: Exploring High-Quality Pseudo Labels for Weakly Supervised Instance Segmentation Tianheng Cheng Xinggang Wang Shaoyu Chen Qian Zhang Wenyu Liu ISeg 30 41 0 11 Oct 2022