label-studio — Label Studio is a multi-type data labeling and annotation tool with standardized output format
データラベル化と注釈化を行うためのツールです。
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「segmentation」の検索結果
175 件データラベル化と注釈化を行うためのツールです。
ultralyticsはYOLO(You Only Look Once)の技術を使用したオブジェクト検出ライブラリで、高い精度を提供している。
supervisionは、機械学習技術を活用して、ユーザー独自のコンピュータビジョンツールを作成することができる。
CVATは、機械学習用の業界標準のデータエンジンです。さまざまなスケールのチームが使用し、さまざまなスケールのデータに対応しています。
イメージを注釈するツール。ポリゴン、長方形、円、線、点などを注釈することができる。
stanzaは、さまざまな言語を処理するための言語処理用ライブラリです。
セマンティックシーケンス分割モデルのライブラリです。
Deepfake detection models often rely on high-quality inputs, fixed inference paths, and computationally expens
Building on the pioneering paper of Kearns, Roth, and Ryu (SODA'26), we study information aggregation in a net
Time series data is very common in many real-world applications and in numerous domains, with increasing inter
Self-supervised learning relies on so-called data augmentations $φ(x)$ of unlabeled datapoints $x$ --- for exa
Gaussian kernel sums are the computational core of maximum mean discrepancies (MMDs), kernel gradient flows, S
Vision transformers typically treat every image token as equally important, yet for most tasks in computer vis
As generative audio models grow in complexity, the computational and ecological costs of synthesizing everyday
The representation chosen for a mathematical operation can affect both its algebraic form and its empirical le
As the scale of video surveillance data outpaces manual annotation capacities, weakly supervised video anomaly
Computer-use agents can execute increasingly complex tasks in graphical interfaces, but their interaction expe
3D perception plays a crucial role in real-world applications such as autonomous driving, robotics, and AR/VR.
Comparing intelligent systems under deployment constraints requires more than predictiveaccuracy.This paper de
Sparse autoencoder (SAE) features are increasingly used to explain and steer language-model behavior, but it r
A merchant's payment processor, ledger, ERP and bank feed are updated by messages that get delayed, duplicated
Urban air quality can vary significantly along transit corridors, necessitating high-resolution monitoring. Th
Catheterisation image processing requires segmentation models that are fast, accurate and explainable. While m
Grounded language-model pipelines can be divided into three stages: selecting an object, retrieving passages f
Memory-based evolutionary algorithms for dynamic optimization often carry a redundant second copy of the genot
Early identification of Alzheimer's disease (AD) remains challenging because established assessment methods ca
Recent advances in Earth Observation representation learning accommodate heterogeneous sensors and missing obs
Hyperspectral and multispectral image fusion (HMIF) aims to reconstruct a high-resolution hyperspectral image
Semantic 3D maps are increasingly constructed automatically for aerial robotics by integrating learned semanti
Acquiring high quality annotated medical image data is critical for training deep learning models; however, an
We present InterSing, a framework for generating realistic 3D head animations for duet singing performances. U
Single domain generalization (SDG) aims to learn a model from one labeled source domain that generalizes to un
Zero-shot 6D pose estimation pipelines increasingly rely on strong downstream pose solvers, but their performa
Multi-modal medical images and clinical reports provide complementary anatomical, functional, and semantic inf
Industrial anomaly detection faces two engineering bottlenecks: memory bank construction latency and inference
Synthetic Aperture Radar (SAR) images have all-weather, day-and-night observation capabilities. However, compa
Visual counting is commonly formulated at the instance level, aiming to estimate how many objects of a queried
Reliable robot-to-human handover requires the robot to infer when the person is ready to receive the object, a
Robotic dressing assistance is a promising solution for supporting older adults with physical impairments in d
Fixed 3D Gaussian Splatting (3DGS) reconstructions provide realistic novel views but lack the traversability c
The FitzHugh-Nagumo (FHN) system serves as a simplified model of neuronal voltage dynamics, capturing the acti
In this paper, the problem of data-driven discovery of nonlinear ordinary differential equations (ODEs) is rec
Parameterised graph theory studies how the complexity of graph-theoretic problems depends on structural parame
This paper presents a low-cost, open experimental platform for research in end-to-end autonomous driving with
Ensuring the security of the power system is essential for stability and reliability, especially in the event
Medical image inpainting has the potential to improve automated brain MRI analysis by reconstructing healthy t
Reconstruction-based anomaly detectors are accurate but opaque: a deep autoencoder flags a sample without tell
We study the allocation of indivisible goods among agents with identical additive valuations, focusing on envy
Unsupervised anomaly detection scores each point of an unlabelled, contaminated sample in a single pass, and i
Institutions practising outcome-based education compute learning outcome attainment routinely, while reviews o
The computation of the Bures-Wasserstein (BW) barycenter of an ensemble of positive definite matrices arises t
Human mobility predictability concerns the best prediction performance attainable from a given target and inpu
Data centers are increasingly optimized by artificial intelligence and, at the same time, increasingly loaded
In this paper we study linear non-Gaussian acyclic models (LiNGAM) when used in federated environments. These
Diffusion reinforcement learning (RL) has recently achieved significant success in post-training image and vid
Semantic ID (SID) generative recommendation predicts the next item by generating a short tuple of discrete tok
Anomaly detection in Internet of Things (IoT) networks presents unique challenges due to the diversity of devi
Restricted eigenvalue (RE) bounds govern stable recovery by norm-regularized estimators. For isotropic sub-Gau
This paper examines the relationship between the parameters of autoencoder models and the statistical properti
Let a finite population of n labelled examples carry a class-weighted loss, with pi*n in a rare positive class
Agent reinforcement learning (RL) increasingly runs through full execution harnesses, and a multi-harness reci
This paper investigates structural priming in language model (LM) production, examining how preceding structur
Video editing spans diverse editing paradigms, yet achieving high-quality instruction-guided and subject-guide
In this work, a novel evaluation scheme built on a generalized variant of the Rand Index measure, namely, the
Procedural instruction following is a basic requirement for controllable language-model systems, especially wh
Long-term human-AI interaction is difficult because the information that guides inference is updated implicitl
Vision-language models (VLMs) are increasingly deployed in multi-turn settings where users may describe visual
Vision foundation models (VFMs) are valuable in data-scarce domains such as surgery, where a single pretrained
Anatomic tracer studies reveal how axon bundles project from an injection site, branch into smaller groups of
Automated aortic segmentation in 4D flow MRI is essential for reproducible hemodynamic assessment but is limit
Visual Place Recognition (VPR) localizes a query image by retrieving database images of the same or nearby pla
Seeing frames in order does not mean representing time. Modern VideoLMs receive ordered video streams, yet the
Dense semantic segmentation allocates computational resources uniformly across the entire image, regardless of
We present ENEAS, a unified, text-promptable method for instance tracking and semantic discovery. Text-prompta
Mirrors are common in real-world images, yet producing geometrically consistent reflections with generative mo
The joint interpretation of metabolic function and anatomical structure is essential for clinical diagnosis in
Labelling vision datasets, especially for segmentation tasks, is a laborious and costly process that stymies n
Accurate segmentation of corneal layers in optical coherence tomography (OCT) is essential for quantitative as
Although document OCR systems perform increasingly well on routine documents, complex formulas, structured tex
In-Context Segmentation (ICS) aims to precisely segment arbitrary semantic concepts, such as objects or parts,
Sampling-based motion planning algorithms are a popular class of trajectory planning algorithm due to their sp
The planning algorithms inside an Autonomous Vehicle (AV) rely on information from on-board sensors whose line
Accurate identification of weld seam geometries is essential for automated robotic post processing operations
Conformal risk control is an emerging framework for the safe deployment of machine learning models with finite
Data-consistent inversion (DCI) constructs probability measures whose push-forward distributions agree with ob
Gaussian-process Bayesian optimization (GP-BO) excels at black-box optimization of costly functions, e.g., hyp
Gaussian graphical models (GGMs) are essential tools for interpretable structure learning. However, in high-di
A fundamental quantity in machine learning is the optimal performance achievable by any model on a given task.
Learning graph structures from data is a fundamental problem that spans a wide range of signal processing and
Generative modeling directly on geometric manifolds can avoid errors introduced by flattening non-Euclidean da
We study a variant of the Thompson Sampling (TS) algorithm, called $α$-TS, for solving stochastic generalized
Vision Transformers (ViTs) typically process every image using a fixed input resolution and model width, even
LiDAR-based semantic segmentation is a core perception module for autonomous vehicles and mobile robots. Despi
Ultra-low-altitude unmanned aerial vehicles (UAVs) require surround vision near buildings, vegetation, and oth
Generating safety-critical scenarios is essential for evaluating autonomous driving systems. However, existing
Autonomous lunar missions require real-time per- ception under three coupled constraints: extreme low-light co
Physical reservoir computing (PRC) refers to the use of a physical dynamical system as a computational resourc
Monolithic world models predict the entire next state at every step, spending capacity re-predicting the stati
Visual fluency in generated video does not imply physical reliability, and a scalar quality score alone is inc
Feature-based newsvendor models use observable covariates to tailor inventory decisions, aiming to balance hol
Generative data augmentation is widely used to mitigate class imbalance, yet its theoretical effect on downstr
Reconstructing a damaged musical fragment is an inverse problem: the observed sequence contains partial inform
Uniform-state discrete diffusion models update all tokens in parallel while keeping every position revisable.
We consider semi-supervised classification from a partially classified sample arising from a two-component Wei
High-dimensional clustering is challenging when component distributions are both heavy-tailed and directionall
Multi-Unmanned Aerial Vehicle (UAV) disaster-response systems require coordinated task assignment and local tr
We present SG-AMP, integrating robust depth completion with input-conditioned uncertainty, persistent panoptic
A long-standing open problem in robot manipulator control is whether global regulation can be achieved by clas
YOLOv5という物体検出アルゴリズムをPyTorchから他の言語に変換できるライブラリ。
CoreNLPはJavaで開発されたNLPツールのセットであり、分割、文分割、名詞認識、パーシング、コorefence、感情分析などを行える。
This work shows that diffusion models learned with standard denoising loss can provide effective global MCMC p
We develop a marginal coordinate test for regression with Euclidean predictors and a random-object response in
Machine learning systems are increasingly corrected while they run, and the decision of when to intervene is i
We estimate the conditional population-risk curve of a realized smooth nonconvex gradient flow from the traini
End-to-end autonomous driving models plan future trajectories from raw sensor input. While earlier driving ben
The downwash wake of a hovering quadrotor governs both the vehicle's own performance and the safe spacing of m
Visual SLAM is commonly evaluated on clean trajectories, although deployment failures are often caused by adve
Test-time reasoning has significantly improved performance in domains ranging from games to language models. H
We study the problem of locating a new homogeneous facility under a prelocated facility. Here, a set of $n$ ag
Envy-freeness up to any good (EFX) and pairwise maximin share (PMMS) are standard local fairness criteria for
Deployed decisions are often optimized once and retained because updates impose operational, regulatory, or sw
Kernel density estimation (KDE) is one of the most fundamental statistical estimators of density functions. It
Instance segmentation of overlapping cells in microscopy remains challenging due to semi-transparent structure
We study a class of product-reference diffusion algorithms for sampling from a discrete distribution. We show
We study kernel ridge regression under anisotropic Gaussian data, where the input covariance decays as a power
We extend the FLOP (fast learning of order and parents) algorithm recently proposed by Wienöbst et al. (2026)
Coordination is a desirable feature in multi-agent systems, ranging from robotic swarms to socioeconomic netwo
Nearest neighbor classification relies fundamentally on how locality is defined, yet conventional $k$-NN impos
Music recommendation relies primarily on two signals: user-item interactions, which fail in the cold-start reg
Joint analyses across multiple institutions are increasingly important in biomedical and epidemiological resea
Model selection becomes particularly challenging under strong predictor dependence and model-class uncertainty
In this paper, we study the problem of augmenting a tiny target sample with a massive auxiliary sample. Utiliz
Two of the most fundamental questions in statistical learning theory are the following: which prediction probl
Point-cloud data routinely captured by modern imaging and sensor technologies provide detailed geometric descr
We study pursuit-evasion games on graphs with a single pursuer and an invisible evader. The pursuer may assign
Driver behavior is heterogeneous, context-dependent, and changes over time, and these properties shape the tra
Exact Bayes prediction enjoys fast predictive regret guarantees, but exact posterior updating or representatio
Functional data analysis is an important statistical field that treats data as random functions. In practice,
The OBABO and BAOAB schemes and the other standard Strang splittings of kinetic (underdamped) Langevin dynamic
We study the sample complexity of learning near-optimal bilateral trade mechanisms. Unlike previous work on le
In this paper, we study alternating regret in online convex optimization (OCO), motivated by the success of al
Machine learning is usually formalized through samples, while the persistent individual to which multiple obse
Generative models are commonly ranked by Fréchet Inception Distance (FID) and Kernel Inception Distance (KID),
ES-HyperNEAT evolves substrate topology through adaptive quadtree subdivision; to our knowledge, no implementa
Discrete diffusion models offer a promising alternative to autoregressive generation by enabling parallel upda
One implicit DDIM inversion step is the cheapest probe of whether a pretrained diffusion model encodes local m
Standard solution concepts for stochastic games, such as Markov perfect equilibrium and Markov coarse correlat
Metric distortion has primarily been studied for social choice functions, which select a single winner from or
As an extension of existing Bayesian persuasion framework with inadequate message mechanism, we study direct r
Streaming systems that maintain a pool of expert models must repeatedly decide whether to reuse an existing ex
Spatial aliasing occurs when two or more distinct locations produce highly similar place-cell representations,
Persistent acoustic monitoring can detect machine faults without physical contact, but always-on inference is
Reinforcement learning (RL) theory fundamentally depends on probability theory through the Markov chain. There
Peer prediction seeks to incentivize agents to truthfully report an observed signal by rewarding joint sets of
Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, w
Budget-constrained advertisers commonly rely on two control mechanisms: pacing scales bids, whereas throttling
We study fair division of indivisible goods when agents' valuations are accessed only through ordinal comparis
In Shapley-Scarf housing markets, Ma (1994) shows that top trading cycles (TTC) is the unique mechanism satisf
We study interval scheduling from the perspective of fair allocation. There are $m$ identical machines and a s
An AI that can only give advice seems safe: the human is always free to ignore it. That is the premise of the
Battery swapping is a rapid way to recharge electric vehicles (EVs). As more and more entities are involved in
このライブラリは、コンピューター ビジョンのための高度なAI解釈と可視化ソリューションです。このライブラリは、CNN、ビジョン トランスフォーム、分類、物体検出、分割、画像類似度など、さまざまなコンピューター ビジョンの
Spiking Transformers model token interactions primarily through spiking self-attention (SSA). However, binary
We study the existence of envy-free up to any item (EFX) allocations of indivisible chores when agents have mo
In this work, we introduce analytical replay experiments to the evolutionary computing community. Replay exper
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Characterising optimisation problem instances is a fundamental part of understanding the behaviour and perform
Spiking Transformers provide a promising paradigm for efficient visual processing with spike-driven computatio
The single-selection prophet inequality is a canonical Bayesian online selection problem in which independent
A bidder can quietly buy a stake in a company before making an offer for it. That stake, a toehold, is suppose
この論文では、Dynamic Multi-Objective Optimizationの問題を解くために、Special Point Skeleton Reconstruction (SPSR)アルゴリズムを提案し、Pa
Generative models can reproduce an observational distribution while encoding an incorrect causal structure. We
Streaming data-driven dynamic multi-objective optimization requires algorithms to track time-varying Pareto fr
Many learning problems require representations that reconcile direct input, nearby structure, and broader cont
この研究では、リカレントニューラルネットワークの構造とメモリの使用量の関係を調べた。結果は、メモリの使用量が減少し、モデルがより効率的に学習することができるというものであり、これは、リカレントニューラルネットワークのパフ