arXiv Machine Learning
Multi-Label Perceptual Bug Detection in Video Games using Deep Learning on Gameplay Footage
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arXiv Computer Vision
Sep 16
RefGlitch-Bench: A Benchmark for Reference-based Gameplay Glitch Detection with Vision-Language Models
arXiv:2604.11082v2 Announce Type: replace Abstract: Visual glitches in video games degrade player experience and perceived quality, yet manual quality assurance cannot keep pace with the growing test...
arXiv Computer Vision
Oct 7
Dataset Biases and Shortcut Learning in Motion-Based AI-Generated Video Detection
The paper examines four leading motion‑based AI‑generated video detectors and finds that three of them suffer from preprocessing and sampling biases that inflate their reported performance. These detectors rely heavily on motion patterns—specifically, the lower inter‑frame movement typical of synthetic videos—so their accuracy drops to near random when tested on datasets lacking this bias or after simple spatial augmentations. In contrast, a frequency‑based detector remains robust across all datasets, indicating that frequency‑domain methods may generalize better for detecting AI‑generated videos.
arXiv AI
Jun 16
Revealing Artifacts via Noise Amplification: A Novel Perspective for AI-Generated Video Detection
arXiv:2606. 16742v1 Announce Type: cross Abstract: With the rapid advancement of video generation models, distinguishing between AI-generated and authentic videos has emerged as a challenging endeavor.
arXiv Computer Vision
Sep 30
A Skill-augmented Agentic Framework and Benchmark for Multi-Video Understanding
arXiv:2603.14733v2 Announce Type: replace Abstract: Multimodal Large Language Models have achieved strong performance in single-video understanding, yet their ability to reason across multiple videos...
arXiv Machine Learning
Aug 31
Post-Training VLMs for Video Mistake Detection
The paper introduces a new protocol, Mistake Detection Video Question Answering (MD‑VQA), to evaluate whether models can determine if a step in a video follows its description, covering both seen and unseen actions. It proposes a post‑training approach for video‑language models that uses a reward function to highlight discrepancies between instructions and video content. Experiments show this method surpasses zero‑shot, fine‑tuned, and other post‑training baselines, especially on unseen procedures, improving performance by up to 11.6% on EP‑VQA.
arXiv Computer Vision
Sep 10
VideoTIR: Accurate Understanding for Long Videos with Efficient Tool-Integrated Reasoning
VideoTIR introduces a reinforcement‑learning approach to improve long‑video understanding by encouraging multimodal large language models to use comprehensive multi‑level toolkits efficiently. It combines Zero‑RL and SFT cold‑starting strategies to help models retrieve and focus on meaningful video segments, images, and regions, thereby reducing hallucinations. The method includes Toolkit Action Grouped Policy Optimization (TAGPO) to streamline tool‑calling and a sandbox‑based trajectory synthesis framework for high‑quality data, achieving strong results on three long‑video QA benchmarks.