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4,009 papers
Evaluation of a video authoring pipeline featuring two layers of structured refusal shows that both layers independently improve the same instructional dimensions, suggesting that thoughtful resistance and generative AI are not opposites but partners.
Doc-CoB (Chain-of-Boxes), a simple-yet-effective framework that integrates coarse-to-fine layout-aware visual reasoning into multimodal large language models, and introduces two reasoning tasks for box recognition and box reasoning.
This work repurposes pretrained video generative models as a unified and data-efficient framework for geometry estimation, formulated innovatively as a next-frames prediction task, and inherits naturally structured knowledge and richer priors from the video model, enabling more data efficient and effective learning of...
A hybrid feature fusion framework, termed DWT_AlexNet_DNN, is proposed, which combines Discrete Wavelet Transform (DWT) features with deep features extracted using AlexNet for texture image classification.
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Across four-fold cross-validation the organ-supervised model achieves the best and most stable performance, the interactive stage improves the Dice score monotonically with each prompt, and PSMA-specific training yields the strongest tracer-wise results.
An Anatomy-Routed Contrastive Learning for 3D Chest CT (ARC-CT), a region-aware framework that addresses limitations using only reports extracted from reports by an LLM, with no manual annotations or bounding boxes, outperforms both comparable efficient baselines and larger transformer models.
A subject-conditioned causal neural surrogate is developed using OpenSim-derived static parameters, temporal joint kinematics, true muscle capacities, and training-only perturbations to establish a leakage-free evaluation and credibility framework for pediatric MSK surrogates, while identifying force modeling and epist...
Qualitative evaluations on heterogeneous medical spectral acquisition systems demonstrate the practical relevance of the proposed training data augmentation protocol as an enabler for spatially coherent spectral fusion in HSI workflows.
This work presents a standardized, open-source benchmark for evaluating state-of-the-art (SOTA) deep learning methods for Earth observation change detection, and reveals that well-optimized classical architectures, such as Siamese U-Nets, frequently outperform more complex contemporary models when computational efficie...
This work presents CheXtriev, a graph-based, anatomy-aware framework for chest radiograph retrieval that leverages graph transformers to extract informative features from specific anatomical regions and captures spatial context and the interplay between anatomical location and findings.
The results suggest medical vision encoders carry a map of where organs usually lie, and little of the machinery for comparing structures within a particular patient, in addition to five architectural configurations and three medical foundation models, frozen and finetuned.
VersaGauss is introduced, a unified framework for generation, simulation, and rendering that supports versatile physics-based dynamic generation, particularly for multiphase interactions, and proposes the Coupled Multiphase Point Method to effectively model and generate multiphase interactions.
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
GPT-Lab
· gpt-lab.eu
Oct 8, 2026
Exploring how generative AI could make machine vision more accessible to businesses. The post GenEye in a Box: Making Machine Vision Something You Can Just Ask For appeared first on GPT-Lab.
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
A Blog post by H company on Hugging Face
A Blog post by Liquid AI on Hugging Face
Computer scientist, entrepreneur, and philanthropist will collaborate with the MIT Schwarzman College of Computing to advance AI and scientific discovery.
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