GleanVID: Complementary Token Selection for Efficient Video Large Language Models

arXiv:2609.37042 · cs.CV, cs.LG · Submitted 2026-09-29 · Read on arXiv

cs.CV, cs.LG

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• Benchmark — 22× in this paper · explained in 2 episodes
• Ablation — 17× in this paper · explained in 1 episode
• Ablation study — 12× in this paper · explained in 1 episode
• Pruning — 6× in this paper · explained in 4 episodes
• Redundancy — 6× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• Local Representativeness Score — 4× in this paper
• Subspace Complementarity Score — 4× in this paper
• Grounding — 3× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Temporal Reasoning — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Aggregation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• CVF International Conference on Computer Vision — 1× in this paper
• Consistency — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Test-time Scaling — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Visual Grounding — 1× in this paper · explained in 2 episodes

Sources

• FlashVID: Efficient Video Large Language Models via Training-free Tree-based Spatiotemporal Token Merging
• V-CAST: Video Curvature-Aware Spatio-Temporal Pruning for Efficient Video Large Language Models
• GPT-4 Technical Report
• Qwen3-VL Technical Report
• SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
• InternVideo2.5: Empowering Video MLLMs with Long and Rich Context Modeling
• VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding

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