arXiv AI
Measuring Similarity between Artistic and AI Generated Images using Siamese Neural Networks
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Preserve-and-Compose Training for Composed Image Retrieval
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Redemption Score: A Multi-Modal Evaluation Framework for Image Captioning via Distributional, Perceptual, and Linguistic Signal Triangulation
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arXiv AI
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Cross-modal Identity Mapping: Minimizing Information Loss in Modality Conversion via Reinforcement Learning
arXiv:2603. 01696v2 Announce Type: replace-cross Abstract: Large Vision-Language Models (LVLMs) often omit or misrepresent critical visual content in generated image captions.
arXiv AI
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CLAY: Conditional Visual Similarity Modulation in Vision-Language Embedding Space
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Hugging Face Trending Papers
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Back to Back with a Copy: A Computational Analysis of AI-Generated Visual Contemporary Art Pastiches
The aim of this paper is twofold. First, it investigates whether newer generative models are getting better at pastiching contemporary artworks.
arXiv AI
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CLIP Embeddings for AI-Generated Image Detection: A Few-Shot Study with Lightweight Classifier
The paper explores whether CLIP embeddings can detect AI-generated images by using a frozen CLIP model to extract visual embeddings and training lightweight classifiers on top. On the CIFAKE benchmark, the approach achieves 95% accuracy without language reasoning, and 85% accuracy after few-shot adaptation with 20% of the data. Certain image types, such as wide-angle photographs and oil paintings, remain challenging, highlighting unexplored difficulties in AI-generated image classification.