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3,315 papers
A Reliability-Aware Boundary Refinement Network (RABR-Net), a two-stage framework for trustworthy image segmentation that provides an interpretable and reliability-focused strategy for boundary-sensitive biomedical image segmentation.
CP-BG-Bench is introduced, a paired-view evaluation framework that holds the central cell fixed across four matched views, ablating or augmenting surrounding pixels as a controlled intervention.
Experiments on seven heterogeneous handwriting benchmarks show that complementary supervision consistently improves training with page transcription alone, while joint multi-source training provides further gains on most datasets, and the proposed sparse expert model further improves the dense baseline on six of the se...
STAG is introduced, to the authors' knowledge the first post-hoc framework for token-level spectro-temporal grounding of captions generated by audio-based MLLMs, and provides behavioral support for the faithfulness and selectivity of the explanations.
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Increasing safety is the primary objective of automated vehicles. Achieving this goal requires reliable safety metrics that incorporate safety-relevant factors such as object type, velocity, and criticality. A key capability of such metrics is the distinction between critical and non-critical objects, which is addresse...
It is suggested that physics-guided synthetic HFUS images contain transferable anatomical and textural cues for real-domain skin layer segmentation, although further reduction of the synthetic-real appearance gap is needed to enable greater gains.
A Geometric-to-Semantic Spherical Transfer Learning framework that outperforms fully supervised baselines trained from scratch on variable and tertiary sulci, confirming that learning the cortex shape is highly beneficial for identifying its rarest parts.
RiPPLE provides a whole-space ranking from a fractional anchor-training budget, with comparisons interpreted under their respective evaluation and cost protocols, and the results examine ranking quality, label efficiency, architecture selection, and the roles of readout, coverage, and propagation.
A dataset-independent metric called the CREDibility Score (CREDS) is proposed for OOD prediction and credibility curves are introduced to study the maximum credibility a model can attain and credibility heat maps are introduced to characterize pre-hoc model behavior across different datasets.
CLAP is a framework for cross-embodiment action-conditioned video generation capable of being trained on diverse, internet-scale videos across human and robotic agents and delivers the most comprehensive suite of action-conditioned video world models to date.
This work proposes DenseTRF, a self-supervised representation adaptation framework based on texture-centric attention that leverages slot attention to learn texture-aware representations that capture invariant visual structures and significantly improves robustness to domain shifts.
A unified conditional-flow framework that casts generation, semantic editing, and intra-structural retargeting as condition-modulated transport within one text- and skeleton-conditioned rectified-flow model, replacing separate pipelines with a single conditional motion transport model while keeping the same-topology re...
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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