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3,462 papers
This work proposes Class-wise Reliability-Aware Distillation (CRAD), which, per class, first discards teachers that disagree with the peer consensus and then takes a weighted average of the rest, weighting each teacher by its per-class reliability (precision, or inverse variance).
The complementary gap pair measures loss on the deployable oracle-estimated pathway and probes sensitivity on the oracle-random pathway to test whether oracle-conditioned performance survives the inference pathway actually available at deployment.
LUNA is the first end-to-end 3D animatable model that supports implicit 2D driving and introduces hybrid supervision that distills soft structural priors from an LBS teacher and a loss that supports training on both limited fitted data and large in-the-wild unlabeled videos.
GeM-NR is proposed, a fast and flexible training-free approach for general multi-view consistent image editing, including edits that drastically change the geometry and appearance of the scene, and is demonstrated to handle edits with significant changes in geometry and appearance.
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This work presents a training-free agentic pipeline for automated brain MRI analysis and demonstrates that agentic AI can solve highly neuro-radiological image analysis tasks through tool use without the need for training or fine-tuning.
DiffHDR is presented, a framework that formulates LDR-to-HDR conversion as a generative radiance inpainting task within the latent space of a video diffusion model that significantly outperforms state-of-the-art approaches in radiance fidelity and temporal stability.
The Flow Divergence Sampler is proposed, a training-free framework that refines intermediate states before each solver step that consistently improves fidelity across various generation tasks including text-to-image synthesis, and inverse problems.
This paper proposes a novel training free token pruning framework grounded in the dual form perspective of attention, and derives a novel metric quantifying both a token's information magnitude and information duplication.
A VLM-as-a-Judge metric for SVG generation, validated through human correlation studies, and an evaluation of frontier VLMs reveals significant performance gaps, positioning VectorGym as a rigorous framework for advancing visual code generation.
Large vision-language models (LVLMs) are increasingly being applied to multi-view image inputs captured from diverse viewpoints. Despite this growing use, current LVLMs often generate incorrect responses due to visual interference from non-target instances or viewpoints, a phenomenon we term multi-view hallucination (M...
V-Co, a systematic study of visual co-denoising in a unified JiT-based framework, outperforms the underlying pixel-space diffusion baseline and strong prior pixel-diffusion methods while using fewer training epochs, offering practical guidance for future representation-aligned generative models.
EvoPrompt is a novel framework designed to explicitly steer the prompt trajectory for knowledge-preserving fine-tuning, and achieves state-of-the-art performance in few-shot learning while robustly preserving the original zero-shot capabilities of pre-trained VLMs.
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.
Human-Computer Interaction and Visualization
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