arXiv AI
Foresight: planning future perception in streaming VLMs without retraining
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arXiv AI
Jun 2
StreamingVLM: Real-Time Understanding for Infinite Video Streams
arXiv:2510. 09608v2 Announce Type: replace-cross Abstract: Vision-language models (VLMs) could power real-time assistants and autonomous agents, but they face a critical challenge: understanding near-infinite video streams without escalating latency and memory usage.
arXiv Computer Vision
5d ago
FlashBack: Knowing When to Remember in Streaming Vision-Language Models
arXiv:2610.01192v1 Announce Type: new Abstract: Streaming vision-language models must process continuously growing video streams under a bounded compute budget, creating a persistent tension between...
arXiv Computer Vision
Sep 2
StreamScout: Learning When to Look Deeper for Streaming Video Understanding
arXiv:2609.00291v1 Announce Type: new Abstract: Streaming video understanding requires answering questions that arrive at arbitrary moments over an unbounded video stream. Existing systems primarily...
arXiv AI
Aug 26
VisCache: Visual KV Cache Pruning for Efficient Vision Large Language Model Inference
VisCache introduces a two-stage, plug‑and‑play framework for pruning visual key‑value caches in Vision Large Language Models without retraining. The first stage filters out temporally redundant keyframes, while the second stage, PruneKV, applies a parabolic layer‑wise budget and asymmetric update to selectively prune keys and fuse values, preserving essential context. Experiments show up to 2.35× speedup and significant memory savings with only 19–28% of the original cache retained, outperforming existing baselines.
arXiv AI
Jun 8
Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding
arXiv:2606. 06991v1 Announce Type: cross Abstract: Online Video Large Language Models (Video-LLMs) have advanced toward seamless human-AI interaction through frame-by-frame processing and proactive responding.
Hugging Face Trending Papers
Aug 13
FlashDrive: Flash Vision-Language-Action Inference for Autonomous Driving
Vision-Language-Action (VLA) models promise to bring end-to-end reasoning to autonomous driving, but their computational cost remains far too high for real-time control. The core challenge is structural: VLA inference is not a single bottleneck but a cascade of four.