4 citations · 8 across the 5 of their papers we have counts for
8 papers · 1 filter
Beyond Masking: Demystifying Token-Based Pre-Training for Vision Transformers
Yunjie Tian, Lingxi Xie, Jiemin Fang +6
The past year has witnessed a rapid development of masked image modeling (MIM). MIM is mostly built upon the vision transformers, which suggests that self-supervised visual represe…
DATA: Domain-Aware and Task-Aware Self-supervised Learning
Qing Chang, Junran Peng, Lingxie Xie +4
The paradigm of training models on massive data without label through self-supervised learning (SSL) and finetuning on many downstream tasks has become a trend recently. However, d…
GAIA: A Transfer Learning System of Object Detection that Fits Your Needs
Xingyuan Bu, Junran Peng, Junjie Yan +2
Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently. However, as the…
Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels
Junran Peng, Xingyuan Bu, Ming Sun +3
Training with more data has always been the most stable and effective way of improving performance in deep learning era. As the largest object detection dataset so far, Open Images…
Learning an Efficient Network for Large-Scale Hierarchical Object Detection with Data Imbalance: 3rd Place Solution to Open Images Challenge 2019
Xingyuan Bu, Junran Peng, Changbao Wang +2
This report details our solution to the Google AI Open Images Challenge 2019 Object Detection Track. Based on our detailed analysis on the Open Images dataset, it is found that the…
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection
Junran Peng, Ming Sun, Zhaoxiang Zhang +2
Recently, Neural Architecture Search has achieved great success in large-scale image classification. In contrast, there have been limited works focusing on architecture search for…