A parameter-free approach drops redundant video tokens via temporal L1 differences in frozen latent space and reconstructs them with LIT, yielding 31x speedup over ElasticTok-CV on TokenBench and DAVIS.
VideoGPT: Video generation using VQ-V AE and transformers.arXiv preprint arXiv:2104.10540, 2021
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Adaptive Tokenisation Via Temporal Redundancy Masking And Latent Inpainting
A parameter-free approach drops redundant video tokens via temporal L1 differences in frozen latent space and reconstructs them with LIT, yielding 31x speedup over ElasticTok-CV on TokenBench and DAVIS.