text:   Cycle-Consistency",6,"['Cycle comes again: we propose contrastive learning with cross-video cycle-consistency. Instead of learning by augmenting a single image, our method forms a cycle across different videos to provide positive training pairs from different instances. \n\n<LINK>\n(1/n) <LINK>', 'Start from one frame in a video, we find its soft nearest neighbor from other videos as a forward step. The cycle-consistency is achieved when the soft nearest neighbor finds its closest frame back within the same video as the start frame. \n\n(2/n) https://t.co/HYxWYKAcki', 'The self-supervised image representation can be generalized to multiple downstream tasks beyond action recognition in videos, including image classification and object tracking. \n\nProject page: https://t.co/Fd4lOmC97M…\n\nJoint work with @Happy_Wu\n\n(3/n)', '@kevin_zakka It needs to have two videos from the same action class to form the cycle. So in the sense of knowing the two videos are from the same action class, it is labeled.', '@kevin_zakka But this is just stating the fact in the TCC experiments. If you are using PennAction category labels to get the pairs, it is ""require human annotators to provide grouth-truth pairs"" no? We are not trying to downgrade TCC, it is great work, and the statement is also neutral.', '@kevin_zakka And I also think it makes perfect sense for TCC to align two videos from the same category since it is performing a frame-level alignment. It will not make sense for TCC to apply on aligning two completely different action videos. For us, we are not trying to align two videos.']",21,05,1510