Tootfinder

💪 Omnivorous? Vegan? Makes no difference to muscle building after weight training, study finds
https://medicalxpress.com/news/2025-04-omnivorous-vegan-difference-muscle-weight.html

Omnivorous? Vegan? Makes no difference to muscle building after weight training, study finds
A new study asked three questions about muscle protein synthesis in response to a nine-day diet and weight training regimen: First, does the source of protein—plant or animal-based—make any difference to muscle gain? Second, does it matter if total daily protein intake is evenly distributed throughout the day? And third, does a moderate but sufficient daily protein intake influence any of these variables? The answer to all three questions is "no," the researchers found.

Roadmap zu Smart Glasses: Was Apple angeblich bei VR- und KI-Brillen plant
Apple arbeitet angeblich an mindestens fünf Computerbrillen, die die Vision-Sparte ankurbeln sollen. Problemkinder sind Siri und KI.

Even de naam vsn een van de personages van Paulus de boskabouter opzoeken.
personages Paulus de boskabouter – Qwant
https://www.qwant.com/
@…
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Spa-VLM: Stealthy Poisoning Attacks on RAG-based VLM
Lei Yu, Yechao Zhang, Ziqi Zhou, Yang Wu, Wei Wan, Minghui Li, Shengshan Hu, Pei Xiaobing, Jing Wang
https://arxiv.org/abs/2505.23828

Spa-VLM: Stealthy Poisoning Attacks on RAG-based VLM
With the rapid development of the Vision-Language Model (VLM), significant progress has been made in Visual Question Answering (VQA) tasks. However, existing VLM often generate inaccurate answers due to a lack of up-to-date knowledge. To address this issue, recent research has introduced Retrieval-Augmented Generation (RAG) techniques, commonly used in Large Language Models (LLM), into VLM, incorporating external multi-modal knowledge to enhance the accuracy and practicality of VLM systems. Nev…

- I found my love in Portofino / Dalida
I found my love in Portofino
Perché nei sogni credo ancor
Lo strano gioco del destino
A Portofino m'ha preso il cuor

Nel dolce incanto del mattino
Il mare ti ha portato a me
Socchiudo gli occhi e a me vicino
A Portofino rivedo te
Ricordo un angolo di cielo
Dove ti stavo ad aspettar
Ricordo il volto tanto amato
E la tua bocca da baciar

This https://arxiv.org/abs/2503.08720 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

AI for Just Work: Constructing Diverse Imaginations of AI beyond "Replacing Humans"
"why" we develop AI. Lacking critical reflections on the general visions and purposes of AI may make the community vulnerable to manipulation. In this position paper, we explore the "why" question of AI. We denote answers to the "why" question the imaginations of AI, which depict our general visions, frames, and mindsets for the prospects of AI. We identify that the prevailing vision in the AI community is largely a monoculture that emphasizes objectives such as replacing humans and improving p…

HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction
Zhonghao Shi, Enyu Zhao, Nathaniel Dennler, Jingzhen Wang, Xinyang Xu, Kaleen Shrestha, Mengxue Fu, Daniel Seita, Maja Matari\'c
https://arxiv.org/abs/2506.20566

HRIBench: Benchmarking Vision-Language Models for Real-Time Human Perception in Human-Robot Interaction
Real-time human perception is crucial for effective human-robot interaction (HRI). Large vision-language models (VLMs) offer promising generalizable perceptual capabilities but often suffer from high latency, which negatively impacts user experience and limits VLM applicability in real-world scenarios. To systematically study VLM capabilities in human perception for HRI and performance-latency trade-offs, we introduce HRIBench, a visual question-answering (VQA) benchmark designed to evaluate VL…

MiCo: Multi-image Contrast for Reinforcement Visual Reasoning
Xi Chen, Mingkang Zhu, Shaoteng Liu, Xiaoyang Wu, Xiaogang Xu, Yu Liu, Xiang Bai, Hengshuang Zhao
https://arxiv.org/abs/2506.22434

MiCo: Multi-image Contrast for Reinforcement Visual Reasoning
This work explores enabling Chain-of-Thought (CoT) reasoning to link visual cues across multiple images. A straightforward solution is to adapt rule-based reinforcement learning for Vision-Language Models (VLMs). However, such methods typically rely on manually curated question-answer pairs, which can be particularly challenging when dealing with fine grained visual details and complex logic across images. Inspired by self-supervised visual representation learning, we observe that images contai…

V2T-CoT: From Vision to Text Chain-of-Thought for Medical Reasoning and Diagnosis
Yuan Wang, Jiaxiang Liu, Shujian Gao, Bin Feng, Zhihang Tang, Xiaotang Gai, Jian Wu, Zuozhu Liu
https://arxiv.org/abs/2506.19610

V2T-CoT: From Vision to Text Chain-of-Thought for Medical Reasoning and Diagnosis
Recent advances in multimodal techniques have led to significant progress in Medical Visual Question Answering (Med-VQA). However, most existing models focus on global image features rather than localizing disease-specific regions crucial for diagnosis. Additionally, current research tends to emphasize answer accuracy at the expense of the reasoning pathway, yet both are crucial for clinical decision-making. To address these challenges, we propose From Vision to Text Chain-of-Thought (V2T-CoT),…

WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts
Negar Foroutan, Angelika Romanou, Matin Ansaripour, Julian Martin Eisenschlos, Karl Aberer, R\'emi Lebret
https://arxiv.org/abs/2506.15594

WikiMixQA: A Multimodal Benchmark for Question Answering over Tables and Charts
Documents are fundamental to preserving and disseminating information, often incorporating complex layouts, tables, and charts that pose significant challenges for automatic document understanding (DU). While vision-language large models (VLLMs) have demonstrated improvements across various tasks, their effectiveness in processing long-context vision inputs remains unclear. This paper introduces WikiMixQA, a benchmark comprising 1,000 multiple-choice questions (MCQs) designed to evaluate cross-…