Dr. Yogesh Rawat’s “Robustness in Sequential Data” workshop has been accepted to CVPR 2022. Most of the real-world data is sequential and there is always a distribution shift when we move from training set to real-world testing scenario. This workshop invites researchers from both academia and industry to advance the research in robust learning for real-world applications. The goal of this workshop is to explore the fundamental problems in the characterization of distribution shifts in sequential data and to develop robust models for sequential data for real-world applications. Please visit the workshop website for more details.
Dr. Chen Chen’s “International Workshop on Federated learning for Computer Vision (FedVision)” was also accepted to CVPR 2022. Federated Learning (FL) has become an important privacy-preserving paradigm in various machine learning tasks. However, the potential of FL in computer vision applications, such as face recognition, person re-identification, and action recognition, is far from being fully exploited. Moreover, FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection and image segmentation, compared to the traditional centralized training paradigm. This workshop aims at bringing together researchers and practitioners with common interests in FL for computer vision and studying the different synergistic relations in this interdisciplinary area. More details (e.g., Call for Papers) can be found on the workshop website: https://sites.google.com/view/fedvision