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3,462 papers
The proposed Cen-Prune is a lightweight, plug-and-play correction, which measures subset diversity using centered cosine similarity while retaining raw-space distinctiveness as a complementary token-wise preference and provides robust improvements in overall performance across existing diversity-based pruners.
The key insight is that temporal understanding benefits from supervised fine-tuning on synthetic data, whereas spatial understanding is better served by pretrained object detectors and physics priors that transfer naturally across domains.
This work shows that the overconfidence appears on unseen scenes even when the model reaches its loss's optimum, andfits a power law on the confidence with two constants per backbone--dataset pair and releases the audit protocol, its results, and the fitted constants per backbone-dataset pair.
MEDSEGBENCHMARKER (MSB), a configuration-driven framework for controlled benchmarking of 2D MIS, provides a practical and extensible basis for making benchmark conditions and evaluation choices explicit and reproducible.
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CineForge is introduced, a self-evolving video-production agent framework that couples CineForge-Produce for video generation with CineForge-Evolve for cross-story policy evolution, and CineForge-Evolve applies Case-to-Pattern-to-Policy Evolution to review trajectory evidence, consolidate recurrent findings into bounde...
A structural face AOI-guided Eye-Gaze Track Network (AOI-Net) that jointly models short-term temporal dynamics and AOI-level structural organization and enables interpretable gaze-behavior modeling and provides a practical basis for scalable AI-driven ASD screening in real-world healthcare.
This work formally identifies this limitation as High-Frequency Trajectory Collapse: supervised fine-tuning converges to the conditional mean of the training distribution, which is dominated by smooth, low-frequency textures, causing high-frequency patterns to become nearly un-sampleable.
Insect pollination is critical for global food production, yet monitoring pollinators at commercial farm scale remains a challenge. Recent advances in computer vision and deep learning have enabled detailed analysis of pollinator behaviour, but monitoring must trade-off detail against spatial coverage and human or tech...
Object detectors are typically trained under closed-set supervision, where unlabeled regions are implicitly treated as background. Under incomplete annotations, this assumption introduces objectness bias: visually valid but unlabeled objects are used as negatives, tying objectness to the annotated taxonomy rather than...
A training-free looping framework that repeatedly applies selected transformer layers inside each denoising call is introduced, which improves primary and auxiliary quality metrics with competitive quality--efficiency trade-offs across two Scale-RAE model scales.
STARLINC, the first ML-based framework for satellite trail removal without requiring tedious pixel-level annotation of astronomical images, is introduced, establishing STARLINC as a scalable solution for next-generation astronomical surveys.
DocIntent, a training-free Answerability-Guided Agentic Restoration framework, which first assesses question answerability, then identifies task-relevant degradations and selectively invokes restoration tools, and which consistently improves the average score and consistency of different open- and closed-source MLLMs.
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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