arXiv Computer Vision

Rule-Constrained Assignment for Cue-Ball Identification in Broadcast Snooker

arXiv Computer Vision
Oct 7

Deep Learning Based Illegal Bowling Action Detection

arXiv:2610.07223v1 Announce Type: new Abstract: Cricket, often referred to as the "gentleman's game," adheres to a strict rule set for both batsmen and bowlers, where each delivery can significantly...

By Debopom Sutradhar, Niful Islam, Sudipto Mondal, Tasmima Hossain Jamim, Jubaer Muhammad Shufol, Swakkhar Shatabda
arXiv AI
Jul 17

SportD: Can VLMs Physically Strategize?

arXiv:2607. 14616v1 Announce Type: new Abstract: Vision--language models have become increasingly capable of interpreting visual scenes, but it remains unclear whether they can use information to make strategically effective decisions.

By Jasin Cekinmez, Addison J. Wu, Haotian Xia, Akshaya Bharadhwaj, Anay Putty, Anirudh Ravishankar, Jaewoong Lee, Jinglin Xiao, Kyumin Andrew Shim, Mishika Ahuja, Nisarga Patil, Leo Liu, Zhuohan Liu, Weining Shen
arXiv AI
Aug 26

Beyond Accuracy: A Dual-Judge Evaluation Protocol for Vision-Language Models in Legally Grounded Tasks

The paper introduces a dual‑judge evaluation protocol for vision‑language models in legally grounded tasks, pairing a 0‑10 quality judge with a strict binary semantic‑equivalence judge. Using a controlled UK traffic‑sign interpretation task, the authors analyze 4,680 evaluations across visibility and occlusion conditions, finding moderate association between judges and an asymmetric Type II error pattern that is most pronounced under heavy occlusion. The protocol requires only one additional LLM call and reveals quality‑trustworthiness signals that single‑judge methods miss.

By Su Myat Noe, Ha Thanh Nguyen, May Myo Zin, Ken Satoh
arXiv AI
Aug 18

CaliBench: Are the Stochastic Dynamics of Video World Models Physically Calibrated?

arXiv:2608. 16829v1 Announce Type: cross Abstract: Video world models approximate the stochastic distribution of physical outcomes through generative sampling, but existing benchmarks score individual generations or compare distributions coarsely over a whole dataset, leaving the fine-grained aleatoric uncertainty of specific phenomena untested.

By Jonathan Sadeghi, Jenny Seidenschwarz, Jesse Allardice, Sirish Srinivasan, Benjamin Graham, Jeffrey Hawke