Tootfinder

SpiderNets: Estimating Fear Ratings of Spider-Related Images with Vision Models
Dominik Pegler, David Steyrl, Mengfan Zhang, Alexander Karner, Jozsef Arato, Frank Scharnowski, Filip Melinscak
https://arxiv.org/abs/2509.04889

SpiderNets: Estimating Fear Ratings of Spider-Related Images with Vision Models
Advances in computer vision have opened new avenues for clinical applications, particularly in computerized exposure therapy where visual stimuli can be dynamically adjusted based on patient responses. As a critical step toward such adaptive systems, we investigated whether pretrained computer vision models can accurately predict fear levels from spider-related images. We adapted three diverse models using transfer learning to predict human fear ratings (on a 0-100 scale) from a standardized da…

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Haoyu Dong, Pengkun Zhang, Mingzhe Lu, Yanzhen Shen, Guolin Ke
https://arxiv.org/abs/2509.06806

MachineLearningLM: Continued Pretraining Language Models on Millions of Synthetic Tabular Prediction Tasks Scales In-Context ML
Large language models (LLMs) possess broad world knowledge and strong general-purpose reasoning ability, yet they struggle to learn from many in-context examples on standard machine learning (ML) tasks, that is, to leverage many-shot demonstrations purely via in-context learning (ICL) without gradient descent. We introduce MachineLearningLM, a portable continued-pretraining framework that equips a general-purpose LLM with robust in-context ML capability while preserving its general knowledge an…

JPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering
Renmiao Chen, Shiyao Cui, Xuancheng Huang, Chengwei Pan, Victor Shea-Jay Huang, QingLin Zhang, Xuan Ouyang, Zhexin Zhang, Hongning Wang, Minlie Huang
https://arxiv.org/abs/2508.05087

JPS: Jailbreak Multimodal Large Language Models with Collaborative Visual Perturbation and Textual Steering
Jailbreak attacks against multimodal large language Models (MLLMs) are a significant research focus. Current research predominantly focuses on maximizing attack success rate (ASR), often overlooking whether the generated responses actually fulfill the attacker's malicious intent. This oversight frequently leads to low-quality outputs that bypass safety filters but lack substantial harmful content. To address this gap, we propose JPS, \underline{J}ailbreak MLLMs with collaborative visual \underl…

Ein großer Meilenstein für die Versorgung mit Medikamenten wurde gerade in Frankreich gegangen. Die Pharma-Firmen, die die Produktion von essentiellen Medikamenten einstellen wollen, müssen eine Alternativ-Versorgung sicherstellen oder alle Rechte an eine öffentliche Einrichtung abgeben. Das sollte in Deutschland unbedingt Nachahmung finden!

Le gouvernement français impose des obligations strictes aux laboratoires qui veulent cesser de produire certa
Pour éviter les pénuries de médicaments d'intérêt thérapeutique majeur, un décret impose désormais des obligations très strictes aux laboratoires qui voudraient arrêter de les produire.

Power corrections to the production of a prompt photon in association with a jet in the $N$-jettiness slicing scheme at NLO QCD
Prem Agarwal, Kirill Melnikov, Ivan Pedron
https://arxiv.org/abs/2510.03097

Power corrections to the production of a prompt photon in association with a jet in the $N$-jettiness slicing scheme at NLO QCD
We compute the next-to-leading-power corrections in the $N$-jettiness variable to the production of a prompt photon and a jet at next-to-leading order in perturbative QCD in the $q \bar q$ annihilation channel. We employ the $k_\perp$ jet algorithm and assume that the $N$-jettiness value divided by the jet transverse momentum is the smallest parameter in the problem; in particular it should be small compared to the jet radius $R$.

From Contrast to Commonality: Audio Commonality Captioning for Enhanced Audio-Text Cross-modal Understanding in Multimodal LLMs
Yuhang Jia, Xu Zhang, Yong Qin
https://arxiv.org/abs/2508.01659

From Contrast to Commonality: Audio Commonality Captioning for Enhanced Audio-Text Cross-modal Understanding in Multimodal LLMs
Audio Captioning (AC) plays a pivotal role in enhancing audio-text cross-modal understanding during the pretraining and finetuning of multimodal large language models (MLLMs). To further strengthen this alignment, recent works have proposed Audio Difference Captioning (ADC), which takes multiple audio inputs and encourages the model to describe their differences, thereby promoting fine-grained audio discrimination. However, despite its effectiveness in enabling difference-telling and detailed d…

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…

InfiMed-Foundation: Pioneering Advanced Multimodal Medical Models with Compute-Efficient Pre-Training and Multi-Stage Fine-Tuning
Guanghao Zhu, Zhitian Hou, Zeyu Liu, Zhijie Sang, Congkai Xie, Hongxia Yang
https://arxiv.org/abs/2509.22261

InfiMed-Foundation: Pioneering Advanced Multimodal Medical Models with Compute-Efficient Pre-Training and Multi-Stage Fine-Tuning
Multimodal large language models (MLLMs) have shown remarkable potential in various domains, yet their application in the medical field is hindered by several challenges. General-purpose MLLMs often lack the specialized knowledge required for medical tasks, leading to uncertain or hallucinatory responses. Knowledge distillation from advanced models struggles to capture domain-specific expertise in radiology and pharmacology. Additionally, the computational cost of continual pretraining with lar…

Can Large Pretrained Depth Estimation Models Help With Image Dehazing?
Hongfei Zhang, Kun Zhou, Ruizheng Wu, Jiangbo Lu
https://arxiv.org/abs/2508.00698 https://

Can Large Pretrained Depth Estimation Models Help With Image Dehazing?
Image dehazing remains a challenging problem due to the spatially varying nature of haze in real-world scenes. While existing methods have demonstrated the promise of large-scale pretrained models for image dehazing, their architecture-specific designs hinder adaptability across diverse scenarios with different accuracy and efficiency requirements. In this work, we systematically investigate the generalization capability of pretrained depth representations-learned from millions of diverse image…

MetaCLIP 2: A Worldwide Scaling Recipe
Yung-Sung Chuang, Yang Li, Dong Wang, Ching-Feng Yeh, Kehan Lyu, Ramya Raghavendra, James Glass, Lifei Huang, Jason Weston, Luke Zettlemoyer, Xinlei Chen, Zhuang Liu, Saining Xie, Wen-tau Yih, Shang-Wen Li, Hu Xu
https://arxiv.org/abs/2507.22062

MetaCLIP 2: A Worldwide Scaling Recipe
Contrastive Language-Image Pretraining (CLIP) is a popular foundation model, supporting from zero-shot classification, retrieval to encoders for multimodal large language models (MLLMs). Although CLIP is successfully trained on billion-scale image-text pairs from the English world, scaling CLIP's training further to learning from the worldwide web data is still challenging: (1) no curation method is available to handle data points from non-English world; (2) the English performance from existin…