activity
20192023
most citedLearning Perceptual Inference by Contrasting

40 citations · 74 across the 9 of their papers we have counts for

collaborators

9 papers

cs.AI20233 cited

Active Reasoning in an Open-World Environment

Manjie Xu, Guangyuan Jiang, Wei Liang +2

Recent advances in vision-language learning have achieved notable success on complete-information question-answering datasets through the integration of extensive world knowledge.…

cs.LG20232 cited

Model-Agnostic Reachability Analysis on Deep Neural Networks

Chi Zhang, Wenjie Ruan, Fu Wang +3

Verification plays an essential role in the formal analysis of safety-critical systems. Most current verification methods have specific requirements when working on Deep Neural Net…

cs.LG20233 cited

XFL: A High Performace, Lightweighted Federated Learning Framework

Hong Wang, Yuanzhi Zhou, Chi Zhang +4

This paper introduces XFL, an industrial-grade federated learning project. XFL supports training AI models collaboratively on multiple devices, while utilizes homomorphic encryptio…

cs.LG20234 cited

Reachability Analysis of Neural Network Control Systems

Chi Zhang, Wenjie Ruan, Peipei Xu

Neural network controllers (NNCs) have shown great promise in autonomous and cyber-physical systems. Despite the various verification approaches for neural networks, the safety ana…

cs.CV2022

CRCNet: Few-shot Segmentation with Cross-Reference and Region-Global Conditional Networks

Weide Liu, Chi Zhang, Guosheng Lin +1

Few-shot segmentation aims to learn a segmentation model that can be generalized to novel classes with only a few training images. In this paper, we propose a Cross-Reference and L…

cs.CV20223 cited

Few-shot Open-set Recognition Using Background as Unknowns

Nan Song, Chi Zhang, Guosheng Lin

Few-shot open-set recognition aims to classify both seen and novel images given only limited training data of seen classes. The challenge of this task is that the model is required…