Special Report, Tech, Americas

We directly address these limitations and propose vCLIMB (video CLass IncreMental Learning Benchmark), a novel benchmark devised for the evaluation of continual learning in video. CLIMB follows the standard class incremental continual learning scenario, but includes some modifications to better fit the nature of video data in human action recognition tasks. We report this as Memory Frame Capacity in our tables to avoid confusion with the memory size defined in image CIL. This means we are not only concerned about selecting the best videos to store in memory, but we also want to identify the best set of frames to keep in memory. Second, since fine-grained temporal video annotations are expensive (especially for long videos), we analyze the effect of using trimmed and untrimmed video data in continual learning. To the best of our knowledge, this is the first work to explore the usage of untrimmed video data in continual learning. After benchmarking these baseline methods, we propose a novel strategy for video continual learning that leverages the inherent temporal consistency in video data to better approach the continual action recognition problem.




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