237 citations · 249 across the 16 of their papers we have counts for
11 papers · 1 filter
Evaluating VLMs for Autonomous Agent-Driven Geometry Clipping Detection in Video Game QA
Carlos Celemin, Benedict Wilkins, Adrián Barahona-Ríos +2
In this work, we study the use of Vision-Language Models (VLMs) for anomaly detection in an agent-driven game Quality Assurance (QA) pipeline focusing on geometry clipping. In this…
TempGlitch: Evaluating Vision-Language Models for Temporal Glitch Detection in Gameplay Videos
Yakun Yu, Ashley Wiens, Adrián Barahona-Ríos +4
Vision-language models (VLMs) are increasingly being explored for video game quality assurance, especially gameplay glitch detection. Most existing evaluations, however, treat glit…
RefGlitch-Bench: A Benchmark for Reference-based Gameplay Glitch Detection with Vision-Language Models
Yakun Yu, Ashley Wiens, Adrián Barahona-Ríos +4
Visual glitches in video games degrade player experience and perceived quality, yet manual quality assurance cannot keep pace with the growing test surface of modern game developme…
Non-Aligned Reference Image Quality Assessment for Novel View Synthesis
Abhijay Ghildyal, Rajesh Sureddi, Nabajeet Barman +2
Evaluating the perceptual quality of Novel View Synthesis (NVS) images remains a key challenge, particularly in the absence of pixel-aligned ground truth references. Full-Reference…
VideoGameQA-Bench: Evaluating Vision-Language Models for Video Game Quality Assurance
Mohammad Reza Taesiri, Abhijay Ghildyal, Saman Zadtootaghaj +2
With video games now generating the highest revenues in the entertainment industry, optimizing game development workflows has become essential for the sector's sustained growth. Re…
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities
Abhijay Ghildyal, Yuanhan Chen, Saman Zadtootaghaj +2
The advent of AI has influenced many aspects of human life, from self-driving cars and intelligent chatbots to text-based image and video generation models capable of creating real…
AIM 2024 Challenge on UHD Blind Photo Quality Assessment
Vlad Hosu, Marcos V. Conde, Lorenzo Agnolucci +3
We introduce the AIM 2024 UHD-IQA Challenge, a competition to advance the No-Reference Image Quality Assessment (NR-IQA) task for modern, high-resolution photos. The challenge is b…
LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment Model
Nasim Jamshidi Avanaki, Abhijay Ghildyal, Nabajeet Barman +1
Recent advancements in the field of No-Reference Image Quality Assessment (NR-IQA) using deep learning techniques demonstrate high performance across multiple open-source datasets.…
MSLIQA: Enhancing Learning Representations for Image Quality Assessment through Multi-Scale Learning
Nasim Jamshidi Avanaki, Abhijay Ghildyal, Nabajeet Barman +1
No-Reference Image Quality Assessment (NR-IQA) remains a challenging task due to the diversity of distortions and the lack of large annotated datasets. Many studies have attempted…
Foundation Models Boost Low-Level Perceptual Similarity Metrics
Abhijay Ghildyal, Nabajeet Barman, Saman Zadtootaghaj
For full-reference image quality assessment (FR-IQA) using deep-learning approaches, the perceptual similarity score between a distorted image and a reference image is typically co…
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
Marcos V. Conde, Saman Zadtootaghaj, Nabajeet Barman +33
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based method…