arXiv:2606.02569v1 Announce Type: cross Abstract: Video is temporally redundant: adjacent frames usually share most objects, background, and layout. Yet existing video multimodal large language models (video MLLMs) usually encode each sampled frame as an independent RGB image, causing visual tokens to repeat content already present in earlier frames. This suggests a more direct video interface: send a full reference frame only when the scene cannot be predicted well from prior context, and otherwise transmit a compact description of inter-frame changes. We call this interface a \emph{predictiv
The accelerating pace of video content generation and consumption, coupled with the computational demands of large multimodal models, necessitates more efficient video encoding techniques right now.
This development proposes a fundamentally more efficient way for AI models to process video, potentially reducing computational costs and improving the performance of video MLLMs significantly.
Existing video MLLMs treat frames independently; AdaCodec introduces a predictive, inter-frame approach, akin to video codecs, making video processing more contextual and less redundant for AI.
- · AI developers
- · Cloud providers
- · Content platforms
- · Users of video MLLMs
- · Inefficient video processing pipelines
- · High-latency video applications
Reduced computational resource usage for video AI applications.
Faster and more sophisticated video analysis, generation, and interaction capabilities for MLLMs.
New classes of AI applications become viable due to lower operational costs and improved real-time processing of video streams.
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Read at arXiv cs.CL