Image recognition, detection, and visual AI
![[2511.21428] From Observation to Action: Latent Action-based Primitive Segmentation for VLA Pre-training in Industrial Settings](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
Abstract page for arXiv paper 2511.21428: From Observation to Action: Latent Action-based Primitive Segmentation for VLA Pre-training in ...
Abstract page for arXiv paper 2511.16719: SAM 3: Segment Anything with Concepts
Abstract page for arXiv paper 2603.28594: Detection of Adversarial Attacks in Robotic Perception
The paper presents the REAL framework, which addresses knowledge conflicts in Knowledge-Intensive Visual Question Answering (KI-VQA) by i...
This article presents VILA, a novel framework for class-incremental learning that utilizes vision-language calibration to enhance efficie...
This paper presents the Optimized Certainty Equivalent Risk-Controlling Prediction Sets (OCE-RCPS), a framework designed to enhance relia...
The paper presents LaySPA, a reinforcement learning framework designed to enhance spatial reasoning in large language models for effectiv...
The paper presents VSAL, a vision-based framework for graph property detection that utilizes adaptive layouts to enhance the detection of...
The paper introduces OneLatent, a framework that compresses reasoning in visual tasks into a single token, significantly reducing output ...
This article evaluates the performance of advanced machine learning architectures on the MNIST-1D dataset, demonstrating their effectiven...
The paper presents Blueprint, a multimodal retrieval system designed to enhance the accessibility of complex engineering drawings and doc...
The paper introduces Lang2Act, a novel framework for enhancing visual reasoning in Vision-Language Models (VLMs) through self-emergent li...
PlotChain introduces a deterministic benchmark for evaluating multimodal large language models (MLLMs) on engineering plot reading, focus...
The article discusses SparseFormer, a new architecture for vision transformers that addresses the compute bottleneck in AI vision models,...
A 2nd year PhD student seeks collaboration opportunities in medical imaging and algorithmic fairness, inviting community members to conne...
This paper presents Proximal-Mean Inversion (PMI), a novel method for stabilizing Rectified-Flow (RF) models, enhancing image reconstruct...
The paper introduces Reinforced Attention Learning (RAL), a novel framework that optimizes internal attention distributions in multimodal...
This article presents a method called Visual Concept Ranking (VCR) to identify visual concepts in large multimodal models, focusing on th...
This article presents a framework for creating standardized multi-layer tissue maps as metadata for AI in digital pathology, enhancing th...
This paper introduces Sample-specific Score-aware Noise Injection (SSNI), a novel framework for diffusion-based adversarial purification ...
This article presents a novel multi-objective reinforcement learning framework for MRI-based sequential decision-making, improving diagno...
This article presents a novel approach to uncertainty estimation in vision-language models (VLMs) by proposing a post-hoc method that enh...
This paper presents the Gauss-Newton method for optimization in shape learning, demonstrating faster convergence and improved accuracy ov...
Get the latest news, tools, and insights delivered to your inbox.
Daily or weekly digest • Unsubscribe anytime