OmniPro is the first benchmark jointly evaluating omni-modal perception, proactive responding, and diverse streaming video understanding tasks using a dual-mode protocol on 2700 samples.
Thinking in streaming video
5 Pith papers cite this work. Polarity classification is still indexing.
5
Pith papers citing it
citation-role summary
background 2
citation-polarity summary
fields
cs.CV 5years
2026 5verdicts
UNVERDICTED 5roles
background 2polarities
background 2representative citing papers
StreamPro introduces a benchmark and training method using CB-Stream Loss and GRPO to enable proactive decision-making in streaming videos, achieving 41.5 on StreamPro-Bench compared to 10.4 previously.
SAVEMem improves streaming video understanding scores by adding semantic awareness to memory compression and query-adaptive retrieval without any model training.
LyraV uses FDTC and SToP for per-frame incremental decoding to reach 98.29% video synchrony at 3.89 FPS while preserving general understanding.
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.
citing papers explorer
-
OmniPro: A Comprehensive Benchmark for Omni-Proactive Streaming Video Understanding
OmniPro is the first benchmark jointly evaluating omni-modal perception, proactive responding, and diverse streaming video understanding tasks using a dual-mode protocol on 2700 samples.
-
StreamPro: From Reactive Perception to Proactive Decision-Making in Streaming Video
StreamPro introduces a benchmark and training method using CB-Stream Loss and GRPO to enable proactive decision-making in streaming videos, achieving 41.5 on StreamPro-Bench compared to 10.4 previously.
-
Semantic-Aware Adaptive Visual Memory for Streaming Video Understanding
SAVEMem improves streaming video understanding scores by adding semantic awareness to memory compression and query-adaptive retrieval without any model training.
-
Don't Pause: Streaming Video-Language Synchrony for Online Video Understanding
LyraV uses FDTC and SToP for per-frame incremental decoding to reach 98.29% video synchrony at 3.89 FPS while preserving general understanding.
-
Toward Native Multimodal Modeling: A Roadmap
A roadmap that defines architectural nativity for multimodal models and categorizes them into Multi-to-Text, Multi-to-Target, and Multi-to-Multi types while outlining an industrial pipeline toward unified transformer-based native multimodal modeling.