7 citations · 18 across the 5 of their papers we have counts for
7 papers
EVA: Exploring the Limits of Masked Visual Representation Learning at Scale
Yuxin Fang, Wen Wang, Binhui Xie +6
We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained to re…
Could Giant Pretrained Image Models Extract Universal Representations?
Yutong Lin, Ze Liu, Zheng Zhang +4
Frozen pretrained models have become a viable alternative to the pretraining-then-finetuning paradigm for transfer learning. However, with frozen models there are relatively few pa…
Correlation-Aware Deep Tracking
Fei Xie, Chunyu Wang, Guangting Wang +3
Robustness and discrimination power are two fundamental requirements in visual object tracking. In most tracking paradigms, we find that the features extracted by the popular Siame…
Pre-Trained Neural Language Models for Automatic Mobile App User Feedback Answer Generation
Yue Cao, Fatemeh H. Fard
Studies show that developers' answers to the mobile app users' feedbacks on app stores can increase the apps' star rating. To help app developers generate answers that are related…
Self-supervised Learning from 100 Million Medical Images
Florin C. Ghesu, Bogdan Georgescu, Awais Mansoor +8
Building accurate and robust artificial intelligence systems for medical image assessment requires not only the research and design of advanced deep learning models but also the cr…
Deep Triplet Quantization
Bin Liu, Yue Cao, Mingsheng Long +2
Deep hashing establishes efficient and effective image retrieval by end-to-end learning of deep representations and hash codes from similarity data. We present a compact coding sol…