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4,009 papers
This work reformulates SYNTAX scoring as a vessel segment identity-preserving anatomical reasoning problem and proposes a hierarchical modeling framework that explicitly aligns learning with the clinical workflow to highlight the necessity of structure-aligned modeling for reliable and stable automated SYNTAX scoring f...
This work introduces Continual Ego, Exo, and Ego-Exo Learning (CE$^4$L), a unified multi-view CL benchmark spanning four representative tasks: cross-view referenced skill assessment, temporal action segmentation, cross-view association, and action anticipation&planning.
PhysReflect is presented, a geometry and perception guided diffusion framework that decodes the predicted clean latent into pixel space at each training step and applies annealed supervision through two complementary differentiable objectives.
This work contributes the first application of Vision Transformers (ViT) and Self-Supervised Learning (SSL) to the shrimp farming domain, addressing both performance bottlenecks and data labeling challenges.
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GrapeSplat amalgamates multi-view cues into a voxel-aligned scene representation and decodes Gaussians directly from the learned grid, requiring no per-scene optimization or post-processing.
QwenVLConnector is presented, a Qwen2.5-VL-based medical chatbot that unifies classification, multi-label classification, textualized detection, counting, regression, and free-form report generation under a single next-token objective.
MM-ContextFold is proposed, a training-free framework that loads raw images only when needed and maintains a persistent, text-only main context for high-level planning and spawns ephemeral branch contexts for image-dependent subtasks.
This review offers a unified synthesis of collaboration architectures and topologies, neural-communication co-design that treats the channel as a differentiable pipeline component, embodied action-perception loops via multi-agent reinforcement learning, and resilience mechanisms for synchronization, uncertainty quantif...
RewardVerse, a rubric-based video reward framework that introduces a dynamic rubric as an intermediate representation between the evaluation query and the scorer, mitigates scalar drift, and provides a robust and interpretable reward signal for RL in video generation.
PACE (Perceptual Agentic Collaborative Evolution), a training-free multi-agent framework that formulates open-ended IQA as explicit protocol construction, is proposed and consistently improves its MLLM backbone, achieving competitive performance across diverse IQA settings, and reduces the Holistic Override Rate.
PAVER, Planning-Aligned BEV Encoder Pretraining is introduced, where from a single LiDAR sweep, PAVER constructs sparse risk and unknown targets describing occupied and unobserved evidence along rule-based ego motions, preserving the downstream architecture and camera-only inference.
SatOV is proposed, a training-free framework for open-vocabulary remote sensing segmentation that restores spatial priors at two stages of the representation pipeline that consistently improves training-free OVS and achieves competitive quantitative and qualitative results against state-of-the-art methods.
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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