FlashBack: Knowing When to Remember in Streaming Vision-Language Models

arXiv:2610.01192 · cs.CV · Submitted 2026-10-01 · Read on arXiv

cs.CV

Submitted: 2026-10-01

Updated: 2026-10-01

Terminology

• Aggregation — 12× in this paper · explained in 1 episode
• Ablation — 10× in this paper · explained in 1 episode
• Benchmark — 9× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 4× in this paper
• KV cache — 4× in this paper · explained in 3 episodes
• Accuracy — 3× in this paper · explained in 1 episode
• Contextual Understanding — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Frozen model — 2× in this paper · explained in 3 episodes
• Semantic Understanding — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic Approaches — 1× in this paper · explained in 1 episode
• Agentic Reasoning — 1× in this paper · explained in 1 episode
• Agentic Retrieval — 1× in this paper · explained in 1 episode
• Agentic Search — 1× in this paper · explained in 1 episode
• Causal Reasoning — 1× in this paper · explained in 2 episodes
• Commentary — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Core — 1× in this paper · explained in 2 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Embeddings — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Memory Augmentation — 1× in this paper · explained in 2 episodes
• State Management — 1× in this paper · explained in 3 episodes
• Training-Free Framework — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Vision-Language Models (VLMs) — 1× in this paper · explained in 6 episodes

Sources

• A Simple Baseline for Streaming Video Understanding
• FOLIO: Focused Semantic Memory for Streaming Video Understanding
• EventMemAgent: Hierarchical Event-Centric Memory for Online Video Understanding with Adaptive Tool Use
• Semantic-Aware Adaptive Visual Memory for Streaming Video Understanding
• What Should a Streaming Video Model Remember?
• Visual Agentic Memory: Enabling Online Long Video Understanding via Online Indexing, Hierarchical Memory, and Agentic Retrieval
• Mage-VL: An Efficient Codec-Native Streaming Multimodal Foundation Model
• ShallowStream: Index Shallow then Answer Deep for Streaming Video Understanding

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