「anomaly」の検索結果
34 件Universal machine-learning interatomic potentials (u-MLIPs) aim to generalize across diverse configurations. B
Token-level text anomaly detection, as an emerging trend of text anomaly detection, moves beyond coarse-graine
Monocular depth estimation is a ubiquitous yet highly ill-posed computer vision task, with downstream applicat
In persistent interactions, long contexts may encode an evolving process rather than a fixed record: later eve
Out-of-distribution (OOD) detection is crucial for safe deployment of medical AI systems, where domain shifts
Object detectors have shown remarkable performance in various fields, among these medical imaging, surveillanc
Low-rank tensor modeling has become an effective tool for hyperspectral anomaly detection. However, existing m
Soft robots offer safe and adaptive interaction with humans and unstructured environments through their inhere
AI agents sometimes act aligned when they infer they are being tested, and differently when not. We argue this
How can vision-language models help video anomaly detection (VAD) when surveillance data remain distributed, w
Smart healthcare monitoring systems require precise action recognition to ensure well-being and timely interve
In this paper, we propose ReactVAU, a Slow-Fast Decoupled Framework for real-time streaming Video Anomaly Unde
Standard Convolutional Neural Networks (CNNs) exhibit severe performance degradation due to a strong inductive
Privacy-sensitive surveillance systems could benefit from large vision-language models (VLMs), but such models
Vision-language models offer a promising approach for zero-shot anomaly detection (ZSAD). However, due to obje
Out-of-distribution (OOD) detection is critical for safe deployment of medical AI systems. Recently, test-time
Deep learning models for CT scan analysis are often limited by the scarcity of precise pixel-level annotations
Progress in video anomaly understanding (VAU) has long been limited by inherent deficiencies of real-world ano
Conditional dimension reduction (cDR) learns low-dimensional latent coordinates while accounting for observed
World-action models (WAM) predict future states to guide robot actions, enabling learning from both action-fre
Large smart-farming deployments generate continuous scientific data from spatially distributed sensors, includ
We present 'RenderFormer-V2', a unified learned transformer-based neural rendering model, complementary to mod
For robots to operate reliably in real-world environments, they need to perceive their surroundings, act, and
We present FlashRender, a few-step generative rendering framework that retakes a source video along a target c
Dynamic networks are being applied in many domains, from social media to logistics systems, each with their ow
Post-hoc out-of-distribution detectors are fitted on a finite reference set, so every score they produce is an
Symbolic Regression (SR) seeks to find succinct mathematical expressions that represent the fundamental relati
Global goodness-of-fit and discrepancy statistics can establish that a sample departs from a reference distrib
We propose a multivariate extension of the pseudo-Voigt profile-a weighted convex combination of Gaussian and
Deep neural networks often exploit spurious associations, a failure known as shortcut learning. Auditing for s
An intuitive method for dimensionality reduction is proposed, which is highly effective for finding interestin
Predictive Coding (PC) is a neural learning paradigm that enables parallelizable neural network layer updates.
Persistent acoustic monitoring can detect machine faults without physical contact, but always-on inference is
An invariant behavioral profile is the defining vulnerability of traditional honeypot installations: a skilled