Tootfinder

KC Concepcion might be a top-40 prospect, Ty Simpson steps up late, more Week 9 reactions https://www.nytimes.com/athletic/6750365/2025/10/26/nfl-draft-2026-reactions-kc-concepcion-ty-simpson/

A Single Neuron Works: Precise Concept Erasure in Text-to-Image Diffusion Models
Qinqin He, Jiaqi Weng, Jialing Tao, Hui Xue
https://arxiv.org/abs/2509.21008 https://

A Single Neuron Works: Precise Concept Erasure in Text-to-Image Diffusion Models
Text-to-image models exhibit remarkable capabilities in image generation. However, they also pose safety risks of generating harmful content. A key challenge of existing concept erasure methods is the precise removal of target concepts while minimizing degradation of image quality. In this paper, we propose Single Neuron-based Concept Erasure (SNCE), a novel approach that can precisely prevent harmful content generation by manipulating only a single neuron. Specifically, we train a Sparse Autoe…

Extracting Conceptual Spaces from LLMs Using Prototype Embeddings
Nitesh Kumar, Usashi Chatterjee, Steven Schockaert
https://arxiv.org/abs/2509.19269 https://

If only the Americans were to demand this kind of alegal accountability of anyone, including presidents – and politicians in general, frankly.
https://www.theguardian.com/world/2025/sep/25/nicolas-sarkoz…

Just another corrupt Trump crony getting a pardon.
https://techcrunch.com/2025/10/23/trump-pardons-binance-founder-changpeng-zhao/

🎶 Hannes Fischer & coss - Schleiermeier
https://song.link/y/CQw81mjbttg

Landmarks, Monuments, and Beacons: Understanding Generative Calls to Action
Victoire Herv\'e, Henrik Warpefelt, Christoph Salge
https://arxiv.org/abs/2509.19030 https://

Landmarks, Monuments, and Beacons: Understanding Generative Calls to Action
Algorithmic evaluation of procedurally generated content struggles to find metrics that align with human experience, particularly for composite artefacts. Automatic decomposition as a possible solution requires concepts that meet a range of properties. To this end, drawing on Games Studies and Game AI research, we introduce the nested concepts of \textit{Landmarks}, \textit{Monuments}, and \textit{Beacons}. These concepts are based on the artefact's perceivability, evocativeness, and Call to Ac…

Mammo-CLIP Dissect: A Framework for Analysing Mammography Concepts in Vision-Language Models
Suaiba Amina Salahuddin, Teresa Dorszewski, Marit Almenning Martiniussen, Tone Hovda, Antonio Portaluri, Solveig Thrun, Michael Kampffmeyer, Elisabeth Wetzer, Kristoffer Wickstr{\o}m, Robert Jenssen
https://arxiv.org/abs/2509.21102

Mammo-CLIP Dissect: A Framework for Analysing Mammography Concepts in Vision-Language Models
Understanding what deep learning (DL) models learn is essential for the safe deployment of artificial intelligence (AI) in clinical settings. While previous work has focused on pixel-based explainability methods, less attention has been paid to the textual concepts learned by these models, which may better reflect the reasoning used by clinicians. We introduce Mammo-CLIP Dissect, the first concept-based explainability framework for systematically dissecting DL vision models trained for mammogra…

The 1st Solution for MOSEv2 Challenge 2025: Long-term and Concept-aware Video Segmentation via SeC
Mingqi Gao, Jingkun Chen, Yunqi Miao, Gengshen Wu, Zhijin Qin, Jungong Han
https://arxiv.org/abs/2509.19183

The 1st Solution for MOSEv2 Challenge 2025: Long-term and Concept-aware Video Segmentation via SeC
This technical report explores the MOSEv2 track of the LSVOS Challenge, which targets complex semi-supervised video object segmentation. By analysing and adapting SeC, an enhanced SAM-2 framework, we conduct a detailed study of its long-term memory and concept-aware memory, showing that long-term memory preserves temporal continuity under occlusion and reappearance, while concept-aware memory supplies semantic priors that suppress distractors; together, these traits directly benefit several MOS…

UniTransfer: Video Concept Transfer via Progressive Spatial and Timestep Decomposition
Guojun Lei, Rong Zhang, Chi Wang, Tianhang Liu, Hong Li, Zhiyuan Ma, Weiwei Xu
https://arxiv.org/abs/2509.21086

UniTransfer: Video Concept Transfer via Progressive Spatial and Timestep Decomposition
We propose a novel architecture UniTransfer, which introduces both spatial and diffusion timestep decomposition in a progressive paradigm, achieving precise and controllable video concept transfer. Specifically, in terms of spatial decomposition, we decouple videos into three key components: the foreground subject, the background, and the motion flow. Building upon this decomposed formulation, we further introduce a dual-to-single-stream DiT-based architecture for supporting fine-grained contro…