Vision Task
Vision tasks, encompassing image and video analysis for diverse applications, are a central focus in computer vision research. Current efforts concentrate on improving model efficiency and robustness, particularly through multi-task learning, the development of novel architectures like Vision Transformers and state-space models, and the incorporation of human feedback for improved alignment with user preferences. These advancements are driving progress in areas such as image compression for machine learning pipelines, multi-image understanding, and the creation of more robust and fair models for real-world deployment.
Papers
Bi-PointFlowNet: Bidirectional Learning for Point Cloud Based Scene Flow Estimation
Wencan Cheng, Jong Hwan Ko
Plex: Towards Reliability using Pretrained Large Model Extensions
Dustin Tran, Jeremiah Liu, Michael W. Dusenberry, Du Phan, Mark Collier, Jie Ren, Kehang Han, Zi Wang, Zelda Mariet, Huiyi Hu, Neil Band, Tim G. J. Rudner, Karan Singhal, Zachary Nado, Joost van Amersfoort, Andreas Kirsch, Rodolphe Jenatton, Nithum Thain, Honglin Yuan, Kelly Buchanan, Kevin Murphy, D. Sculley, Yarin Gal, Zoubin Ghahramani, Jasper Snoek, Balaji Lakshminarayanan