VETO: Video Efficient Token Optimization for Vision Language Models

arXiv:2610.01785 · cs.CV, cs.AI, cs.CL · Submitted 2026-10-01 · Read on arXiv

cs.CV, cs.AI, cs.CL

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Accuracy — 28× in this paper · explained in 1 episode
• Benchmark — 14× in this paper · explained in 2 episodes
• Redundancy — 12× in this paper · explained in 2 episodes
• Ablation — 9× in this paper · explained in 1 episode
• Pruning — 5× in this paper · explained in 4 episodes
• Video Efficient Token Optimization — 5× in this paper
• Robustness — 3× in this paper · explained in 5 episodes
• Ablation Studies — 2× in this paper · explained in 1 episode
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Transformer — 2× in this paper · explained in 2 episodes
• Adaptive Selection — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Multimodal Reasoning — 1× in this paper · explained in 5 episodes
• Resource Constraints — 1× in this paper · explained in 3 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• Token Merging: Your ViT But Faster
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling
• LLaVA-OneVision: Easy Visual Task Transfer
• VideoChat: Chat-Centric Video Understanding
• Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models
• LLaVA-PruMerge: Adaptive Token Reduction for Efficient Large Multimodal Models
• LongLLaVA: Scaling Multi-modal LLMs to 1000 Images Efficiently via a Hybrid Architecture
• Long Context Transfer from Language to Vision

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