arXiv Machine Learning
CodecSight: Leveraging Video Codec Signals for Efficient Streaming VLM Inference
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arXiv AI
Jun 2
AdaCodec: A Predictive Visual Code for Video MLLMs
arXiv:2606. 02569v1 Announce Type: cross Abstract: Video is temporally redundant: adjacent frames usually share most objects, background, and layout.
arXiv Computer Vision
Oct 1
RESUME: Recurrent State Updates from Motion and Residual Signals for Efficient Video Language Modeling
arXiv:2609.39563v1 Announce Type: new Abstract: Existing video language models encode sampled RGB frames independently, so a long video must either exhaust the token budget or drop the changes betwee...
arXiv Computer Vision
Aug 31
Visual Token Coding for Video Multimodal Large Language Models
The paper introduces Visual Token Coding (VTC), a token compression method for video multimodal large language models that mimics classical video coding by predicting I/P frames and measuring residuals to reduce token redundancy. VTC is extended with dynamic features—Dynamic Resolution Input, Dynamic Token Allocation, and Spatial Coverage Top‑K—forming VTC_Dy, which can be applied to existing MLLMs without additional tuning. Experiments on three MLLMs and multiple video benchmarks show that VTC_Dy retains over 100% of average performance with a 50% token budget and 97.8% with a 25% budget, while the code is publicly available.
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 Machine Learning
Oct 22
Cache Locally, Recompute Selectively for Streaming Video Understanding
The paper introduces FrameBlend, a plug‑and‑play technique for streaming video understanding that selectively recomputes key‑value (KV) caches at query time. By re‑prefilling only query‑relevant video tokens, FrameBlend allows shorter attention windows during idle prefill, cutting prefill cost while boosting accuracy. Experiments on three SVU frameworks and five benchmarks show up to 3.9‑point accuracy gains and significant reductions in FLOPs and processing time.
arXiv Computer Vision
Aug 31
Token-Budget Distillation: Transferring Full-Token Semantics to Compressed Video Vision-Language Models
Token-Budget Distillation (TBD) is a parameter‑efficient fine‑tuning framework that adapts video vision‑language models to a fixed token budget. It freezes the pretrained backbone, updates only LoRA adapters, and incorporates FlashVID visual token compression. TBD uses a dual‑path teacher‑student design with full‑token supervision and compressed student optimization, enabling the student to recover full‑token semantics while remaining efficient under aggressive token reduction.