activity
20172023
most citedDistilling Object Detectors with Task Adaptive Regularization

44 citations · 125 across the 14 of their papers we have counts for

collaborators

23 papers

cs.CV20235 cited

Towards AGI in Computer Vision: Lessons Learned from GPT and Large Language Models

Lingxi Xie, Longhui Wei, Xiaopeng Zhang +4

The AI community has been pursuing algorithms known as artificial general intelligence (AGI) that apply to any kind of real-world problem. Recently, chat systems powered by large l…

cs.CV20228 cited

Motion-inductive Self-supervised Object Discovery in Videos

Shuangrui Ding, Weidi Xie, Yabo Chen +4

In this paper, we consider the task of unsupervised object discovery in videos. Previous works have shown promising results via processing optical flows to segment objects. However…

cs.CV20221 cited

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…

cs.CV20211 cited

Multi-dataset Pretraining: A Unified Model for Semantic Segmentation

Bowen Shi, Xiaopeng Zhang, Haohang Xu +4

Collecting annotated data for semantic segmentation is time-consuming and hard to scale up. In this paper, we for the first time propose a unified framework, termed as Multi-Datase…

cs.CV20216 cited

What Is Considered Complete for Visual Recognition?

Lingxi Xie, Xiaopeng Zhang, Longhui Wei +2

This is an opinion paper. We hope to deliver a key message that current visual recognition systems are far from complete, i.e., recognizing everything that human can recognize, yet…

cs.CV20214 cited

Semi-supervised Contrastive Learning with Similarity Co-calibration

Yuhang Zhang, Xiaopeng Zhang, Robert. C. Qiu +3

Semi-supervised learning acts as an effective way to leverage massive unlabeled data. In this paper, we propose a novel training strategy, termed as Semi-supervised Contrastive Lea…