arXiv Computer Vision
An Evolutionary Agentic Approach for Open-ended Image Quality Perception
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Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
arXiv:2608. 09666v1 Announce Type: new Abstract: Recent advances in visual generative models have enabled high-quality image and video generation, but evaluating these models often demands sampling hundreds or thousands of images or videos, which is computationally expensive.
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NoteVQA: Benchmarking VLMs on Real-Life Questions from Human Communities
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arXiv Computer Vision
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Bridging Adversarial and Collaborative Learning for AI-Generated Image Quality Assessment
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arXiv AI
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Debiasing Text-to-Image Evaluation via Implicit Cultural Alignment Reward Modeling
arXiv:2607. 15740v1 Announce Type: cross Abstract: As Text-to-Image (T2I) systems rapidly advance, evaluating the cultural authenticity of synthesized content has become increasingly important for fair and trustworthy generative AI.
arXiv Computer Vision
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arXiv AI
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Perception, Verdict, and Evolution: Hindsight-Driven Self-Refining Forensics Agent for AI-Generated Image Detection
arXiv:2606. 26552v1 Announce Type: cross Abstract: The rapid advancement of generative models presents a significant challenge to existing deepfake detection methods, particularly given the widespread dissemination of highly realistic AI-generated images.