arXiv Computer Vision
Controllable Multi-label Video Safety Detection via Adaptive Tversky Policy Optimization
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
arXiv AI
Jun 6
UNIVID: Unified Vision-Language Model for Video Moderation
arXiv:2606. 05748v1 Announce Type: cross Abstract: Global-scale video moderation faces a dual challenge: the need for fine-grained multi-modal reasoning and the demand for interpretable outputs to support downstream enforcement.
arXiv Computer Vision
Sep 23
Test-time Reinforcement Learning for Anomalous Video Understanding
The paper introduces a test‑time reinforcement learning framework for anomalous video understanding, addressing challenges such as unreliable pseudo‑labels, inadequate reward design, and collapsed group‑relative advantages. It proposes dual‑query consistency filtering, an entropy‑aware consensus reward, and a virtual negative anchor mechanism to improve sample reliability, reward quality, and policy‑gradient signals. Experiments on VAU‑Bench demonstrate significant performance gains, especially on the ECVA subset where accuracy rises from 75.81% to 90.00%.
arXiv Machine Learning
Jun 3
VLESA: Vision-Language Embodied Safety Agent for Human Activity Monitoring
arXiv:2606. 03954v1 Announce Type: cross Abstract: As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible consequences that digital errors do not.
arXiv Computation and Language
Aug 25
SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening
arXiv:2605.17610v2 Announce Type: replace-cross Abstract: The rapid growth of online video platforms and AI-generated content has made reliable video guardrails a key challenge for safety and real-wo...
arXiv AI
Jul 8
PolicyShiftGuard: Benchmarking and Improving Policy-Adaptive Image Guardrails
arXiv:2607. 05910v1 Announce Type: cross Abstract: Image guardrails are typically trained and evaluated under a fixed safety policy, implicitly treating safety as an intrinsic property of an image.
Hugging Face Trending Papers
Jun 2
VLESA: Vision-Language Embodied Safety Agent for Human Activity Monitoring
As AI systems increasingly assist humans in physical tasks, ensuring safety becomes paramount -- physical actions carry immediate and irreversible consequences that digital errors do not. We introduce the Vision-Language Embodied Safety Agent (VLESA), a framework that monitors human activities from egocentric video and triggers real-time safety interventions when dangerous actions are predicted.