Tootfinder

Dienstag: Paypal poliert sein Image auf, SpaceX auf Einkaufstour
Paypal verteilt Gutschriften Starlink erhält Mobilfunkfrequenzen Volksverschlüsselung vor dem Aus Signal mit Cloud-Backup Milliarden für Trumps Familie

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward
Yufeng Cheng, Wenxu Wu, Shaojin Wu, Mengqi Huang, Fei Ding, Qian He
https://arxiv.org/abs/2509.06818

UMO: Scaling Multi-Identity Consistency for Image Customization via Matching Reward
Recent advancements in image customization exhibit a wide range of application prospects due to stronger customization capabilities. However, since we humans are more sensitive to faces, a significant challenge remains in preserving consistent identity while avoiding identity confusion with multi-reference images, limiting the identity scalability of customization models. To address this, we present UMO, a Unified Multi-identity Optimization framework, designed to maintain high-fidelity identit…

Thermodynamics of data
Borko Stosic
https://arxiv.org/abs/2510.06818 https://arxiv.org/pdf/2510.06818

Thermodynamics of data
The recently introduced concept of generalized thermodynamics is explored here in the context of 1d, 2d and 3d data analysis, performed on samples drawn from a 3d X-ray soil sample image. Different threshold levels are used to binarize the 3d sample, wherefrom relative frequencies of binary patterns are found and then used to address finite size scaling behavior of the response functions as a function of the disorder parameter (equivalent of temperature in thermodynamics). It is found that for …

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
Wen Ye, Zhaocheng Liu, Yuwei Gui, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang
https://arxiv.org/abs/2510.07217

GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation
Text-to-image synthesis has made remarkable progress, yet accurately interpreting complex and lengthy prompts remains challenging, often resulting in semantic inconsistencies and missing details. Existing solutions, such as fine-tuning, are model-specific and require training, while prior automatic prompt optimization (APO) approaches typically lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. Meanwhile, test-time scaling methods opera…

EU - Klimaschutz - Ja, äh nein, nicht so streng.....
https://www.sueddeutsche.de/meinung/eu-klimaschutz-emissionen-un-klimakonferenz-li.3334704

Neon: Negative Extrapolation From Self-Training Improves Image Generation
Sina Alemohammad, Zhangyang Wang, Richard G. Baraniuk
https://arxiv.org/abs/2510.03597 https://

Neon: Negative Extrapolation From Self-Training Improves Image Generation
Scaling generative AI models is bottlenecked by the scarcity of high-quality training data. The ease of synthesizing from a generative model suggests using (unverified) synthetic data to augment a limited corpus of real data for the purpose of fine-tuning in the hope of improving performance. Unfortunately, however, the resulting positive feedback loop leads to model autophagy disorder (MAD, aka model collapse) that results in a rapid degradation in sample quality and/or diversity. In this pape…

Aesthetic Image Captioning with Saliency Enhanced MLLMs
Yilin Tao, Jiashui Huang, Huaze Xu, Ling Shao
https://arxiv.org/abs/2509.04378 https://arxiv.org/pd…

Aesthetic Image Captioning with Saliency Enhanced MLLMs
Aesthetic Image Captioning (AIC) aims to generate textual descriptions of image aesthetics, becoming a key research direction in the field of computational aesthetics. In recent years, pretrained Multimodal Large Language Models (MLLMs) have advanced rapidly, leading to a significant increase in image aesthetics research that integrates both visual and textual modalities. However, most existing studies on image aesthetics primarily focus on predicting aesthetic ratings and have shown limited ap…