D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation

2 December 2022

Vladimir Pavlovic

Papers citing "D2DF2WOD: Learning Object Proposals for Weakly-Supervised Object Detection via Progressive Domain Adaptation"

3 / 3 papers shown

Title
Detect, Augment, Compose, and Adapt: Four Steps for Unsupervised Domain Adaptation in Object Detection M. L. Mekhalfi Davide Boscaini Fabio Poiesi 30 6 0 29 Aug 2023
Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation Golnaz Ghiasi Huayu Chen A. Srinivas Rui Qian Nayeon Lee E. D. Cubuk Quoc V. Le Barret Zoph ISeg 252 968 0 13 Dec 2020
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,198 0 01 Sep 2014