AI BriefingResearch on an Adaptive Mechanism to Improve Video Tokenization Efficiency
Key point
This research dramatically increases inference speed by dynamically allocating tokens leveraging the temporal redundancy of video.
Details
Existing video tokenization methods had a limitation in that estimating the amount of information incurred additional computational cost. This study proposes a method that directly leverages the Temporal redundancy inherent in the latent space of a fixed video tokenizer.
The core mechanism is as follows:
- Parameter-free adaptive token allocation: By applying a fixed threshold to the latent representation difference between adjacent frames (Temporal-L1 difference), tokens at positions with little change are automatically removed.
- Latent Inpainting Transformer (LIT): To restore information at removed positions, a lightweight spatiotemporal attention structure called LIT was introduced.
This framework operates with just a single encoder pass and a forward pass of LIT, making it highly efficient. Experimental results demonstrated high reconstruction quality while achieving inference speeds 31x faster than the existing continuous adaptive baseline (ElasticTok-CV) and 2x faster than the discrete information-theoretic baseline (InfoTok).
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