How does the inference throughput and token count scaling of adaptive token pruning (CAT-like) methods compare

Abstract

Abstract: In this paper, we introduce PruneVid, a visual token pruning method designed to enhance the efficiency of multi-modal video understanding. Large Language Models (LLMs) have shown promising performance in video tasks due to their extended capabilities in comprehending visual modalities. However, the substantial redundancy in video data presents significant computational challenges for LLMs. To address this issue, we introduce a training-free method that 1) minimizes video redundancy by merging spatial-temporal tokens, and 2) leverages LLMs' reasoning capabilities to selectively prune visual fea

Research Question

How does the inference throughput and token count scaling of adaptive token pruning (CAT-like) methods compare to fixed tokenization in video diffusion transformers across varying scene complexity, measured in FPS and latency on 4K resolution benchmarks?

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