REVIEW 7 cited by
PruneVid: Visual Token Pruning for Efficient Video Large Language Models
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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 features relevant to question tokens, enhancing model efficiency. We validate our method across multiple video benchmarks, which demonstrate that PruneVid can prune over 80% of tokens while maintaining competitive performance combined with different model networks. This highlights its superior effectiveness and efficiency compared to existing pruning methods. Code: https://github.com/Visual-AI/PruneVid.
Forward citations
Cited by 7 Pith papers
-
PhyCheck: Fine-Grained Evidence-Grounded Dataset for Physical Law Understanding in Video-LLMs
PhyCheck is a 69,825-pair video QA benchmark that tests and improves Video-LLMs' ability to judge whether events obey physical laws, with fine-grained evidence questions and a context-sensitivity pilot.
-
TrajTok: Learning Trajectory Tokens enables better Video Understanding
TrajTok learns to tokenize video into object-trajectory tokens end-to-end, improving video CLIP, probing, and VLM performance over patch and token-merging baselines.
-
CRAFT: Compression via Recursive Adaptive Fusion of Video Tokens for Vision-Language Models
CRAFT recursively merges video tokens with training-free similarity selection plus learnable gated fusion, retaining ~97% of average accuracy at 8x compression across six benchmarks.
-
MMG-Vid: Maximizing Marginal Gains at Segment-level and Token-level for Efficient Video LLMs
MMG-Vid prunes up to 75% of visual tokens in video LLMs with near-lossless accuracy by allocating budgets to segments and selecting temporally novel, spatially diverse tokens.
-
Multi-Granular Spatio-Temporal Token Merging for Training-Free Acceleration of Video LLMs
STTM is a training-free token merging method that reduces video LLM token counts by 50-70% while keeping accuracy nearly unchanged, yielding 2-3x speedups on six video QA benchmarks.
-
LLaVA-Scissor: Token Compression with Semantic Connected Components for Video LLMs
A training-free token compression method using semantic connected components in space and time keeps video understanding accuracy high even when retaining only 5-10% of visual tokens.
-
Spectral Origins of the Self-Correction Blind Spot in Autoregressive Generation
Self-correction blind spots in residual-stream autoregressive models arise iff the product of attention Jacobians has spectral radius ≥1, with a sharp marker threshold and RL coupling condition derived from that radius.
Discussion (0). Sign in to comment.