DART routes zero-shot video temporal grounding queries by difficulty using DPP entropy, achieving up to 3.5 mIoU gains with 7x fewer frames on Charades-STA and ActivityNet Captions.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition
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SurgOnAir introduces a streaming vision-language model trained on a hierarchical surgical dataset to generate real-time, multi-level narrations with explicit transition tokens.
LATERN reformulates video anomaly detection as temporal evidence aggregation via context-aware scoring (CEA) and recursive aggregation (REA) to improve accuracy and coherence for frozen VLMs on benchmarks like UCF-Crime.
citing papers explorer
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DART: Difficulty-Adaptive Routing for Zero-Shot Video Temporal Grounding
DART routes zero-shot video temporal grounding queries by difficulty using DPP entropy, achieving up to 3.5 mIoU gains with 7x fewer frames on Charades-STA and ActivityNet Captions.
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SurgOnAir: Hierarchy-Aware Real-Time Surgical Video Commentary
SurgOnAir introduces a streaming vision-language model trained on a hierarchical surgical dataset to generate real-time, multi-level narrations with explicit transition tokens.
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LATERN: Test-Time Context-Aware Explainable Video Anomaly Detection
LATERN reformulates video anomaly detection as temporal evidence aggregation via context-aware scoring (CEA) and recursive aggregation (REA) to improve accuracy and coherence for frozen VLMs on benchmarks like UCF-Crime.