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Daily Arxiv
Daily Arxiv
世界中で発行される人工知能関連の論文をまとめるページです。
このページはGoogle Geminiを活用して要約し、非営利で運営しています。
論文の著作権は著者および関連機関にあり、共有する際は出典を明記してください。
CTA: Cross-Task Alignment for Better Test Time Training
OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model
Classification of autoimmune diseases from Peripheral blood TCR repertoires by multimodal multi-instance learning
What's Making That Sound Right Now? Video-centric Audio-Visual Localization
LoSiA: Efficient High-Rank Fine-Tuning via Subnet Localization and Optimization
Domain Generalizable Portrait Style Transfer
StreamDiT: Real-Time Streaming Text-to-Video Generation
From Video to EEG: Adapting Joint Embedding Predictive Architecture to Uncover Visual Concepts in Brain Signal Analysis
BMMR: A Large-Scale Bilingual Multimodal Multi-Discipline Reasoning Dataset
Neural-Network solver of ideal MHD equilibria
RAG-R1: Incentivize the Search and Reasoning Capabilities of LLMs through Multi-query Parallelism
Evaluating AI Counseling in Japanese: Counselor, Client, and Evaluator Roles Assessed by Motivational Interviewing Criteria
Hita: Holistic Tokenizer for Autoregressive Image Generation
Empirical Analysis Of Heuristic and Approximation Algorithms for the The Mutual-Visibility Problem
Horus: A Protocol for Trustless Delegation Under Uncertainty
Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-hot Subsurface Understanding
SurgiSR4K: A High-Resolution Endoscopic Video Dataset for Robotic-Assisted Minimally Invasive Procedures
WATS: Calibrating Graph Neural Networks with Wavelet-Aware Temperature Scaling
IPFormer-VideoLLM: Enhancing Multi-modal Video Understanding for Multi-shot Scenes
Tailored Conversations beyond LLMs: A RL-Based Dialogue Manager
Enhancing Generalization of Spiking Neural Networks Through Temporal Regularization
Instruction Following by Boosting Attention of Large Language Models
Evaluating Logit-Based GOP Scores for Mispronunciation Detection
LLMs on support of privacy and security of mobile apps: state of the art and research directions
On the Fundamental Impossibility of Hallucination Control in Large Language Models
Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting
CuVSLAM: CUDA accelerated visual odometry and mapping
Enhancing GOP in CTC-Based Mispronunciation Detection with Phonological Knowledge
An empirical study of task and feature correlations in the reuse of pre-trained models
EEG2TEXT-CN: An Exploratory Study of Open-Vocabulary Chinese Text-EEG Alignment via Large Language Model and Contrastive Learning on ChineseEEG
Hume: Introducing System-2 Thinking in Visual-Language-Action Model
Towards General Continuous Memory for Vision-Language Models
Common Data Format (CDF): A Standardized Format for Match-Data in Football (Soccer)
Bayesian Hierarchical Invariant Prediction
Fine-tuning Diffusion Policies with Backpropagation Through Diffusion Timesteps
Enhancing Satellite Object Localization with Dilated Convolutions and Attention-aided Spatial Pooling
Overcoming Data Scarcity in Generative Language Modelling for Low-Resource Languages: A Systematic Review
The GenAI Generation: Student Views of Awareness, Preparedness, and Concern
Variational OOD State Correction for Offline Reinforcement Learning
Heat Diffusion Models - Interpixel Attention Mechanism
NoWag: A Unified Framework for Shape Preserving Compression of Large Language Models
Offline Learning and Forgetting for Reasoning with Large Language Models
Redefining Evaluation Standards: A Unified Framework for Evaluating the Korean Capabilities of Language Models
PVChat: Personalized Video Chat with One-Shot Learning
Challenges and Trends in Egocentric Vision: A Survey
Eyes on the Environment: AI-Driven Analysis for Fire and Smoke Classification, Segmentation, and Detection
Analytic Subspace Routing: How Recursive Least Squares Works in Continual Learning of Large Language Model
A Survey on Transformer Context Extension: Approaches and Evaluation
Ethical AI for Young Digital Citizens: A Call to Action on Privacy Governance
UniCombine: Unified Multi-Conditional Combination with Diffusion Transformer
The Algorithmic State Architecture (ASA): An Integrated Framework for AI-Enabled Government
A Cascading Cooperative Multi-agent Framework for On-ramp Merging Control Integrating Large Language Models
Zero-shot Medical Event Prediction Using a Generative Pre-trained Transformer on Electronic Health Records
GMLM: Bridging Graph Neural Networks and Language Models for Heterophilic Node Classification
Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association
Enhancing LLM Reliability via Explicit Knowledge Boundary Modeling
RSPO: Regularized Self-Play Alignment of Large Language Models
Fine-Grained Knowledge Structuring and Retrieval for Visual Question Answering
Efficient Risk-sensitive Planning via Entropic Risk Measures
Bayesian Optimization for Controlled Image Editing via LLMs
Low-Rank and Sparse Model Merging for Multi-Lingual Speech Recognition and Translation
Composable Strategy Framework with Integrated Video-Text based Large Language Models for Heart Failure Assessment
Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions
A Theory for Conditional Generative Modeling on Multiple Data Sources
Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport
Scalable Discrete Diffusion Samplers: Combinatorial Optimization and Statistical Physics
DeepCell: Self-Supervised Multiview Fusion for Circuit Representation Learning
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play
ViGiL3D: A Linguistically Diverse Dataset for 3D Visual Grounding
Holistic Construction Automation with Modular Robots: From High-Level Task Specification to Execution
Aria-UI: Visual Grounding for GUI Instructions
RandAR: Decoder-only Autoregressive Visual Generation in Random Orders
Pretrained Reversible Generation as Unsupervised Visual Representation Learning
Pre-Training Graph Contrastive Masked Autoencoders are Strong Distillers for EEG
Random Walks with Tweedie: A Unified View of Score-Based Diffusion Models
Coarse-to-fine Q-Network with Action Sequence for Data-Efficient Robot Learning
Advancing Stroke Risk Prediction Using a Multi-modal Foundation Model
An AI Theory of Mind Will Enhance Our Collective Intelligence
Are LLMs Prescient? A Continuous Evaluation using Daily News as the Oracle
Longitudinal Ensemble Integration for sequential classification with multimodal data
Improving Trust Estimation in Human-Robot Collaboration Using Beta Reputation at Fine-grained Timescales
Feint and Attack: Attention-Based Strategies for Jailbreaking and Protecting LLMs
The Nexus of AR/VR, AI, UI/UX, and Robotics Technologies in Enhancing Learning and Social Interaction for Children with Autism Spectrum Disorders: A Systematic Review
What Would You Ask When You First Saw $a^2+b^2=c^2$? Evaluating LLM on Curiosity-Driven Questioning
Liability and Insurance for Catastrophic Losses: the Nuclear Power Precedent and Lessons for AI
Insuring Uninsurable Risks from AI: The State as Insurer of Last Resort
Empirical evidence of Large Language Model's influence on human spoken communication
The Perils of Optimizing Learned Reward Functions: Low Training Error Does Not Guarantee Low Regret
From LLMs to Actions: Latent Codes as Bridges in Hierarchical Robot Control
Curvature-Aligned Federated Learning (CAFe): Harmonizing Loss Landscapes for Fairness Without Demographics
CoDy: Counterfactual Explainers for Dynamic Graphs
Optimal Transport for Domain Adaptation through Gaussian Mixture Models
Learning Federated Neural Graph Databases for Answering Complex Queries from Distributed Knowledge Graphs
Detecting value-expressive text posts in Russian social media
Deep neural networks have an inbuilt Occam's razor
TT-TFHE: a Torus Fully Homomorphic Encryption-Friendly Neural Network Architecture
SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?
MedGemma Technical Report
Rule Learning for Knowledge Graph Reasoning under Agnostic Distribution Shift
Activation Steering for Chain-of-Thought Compression
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CuVSLAM: CUDA accelerated visual odometry and mapping
Created by
Haebom
作者
Alexander Korovko, Dmitry Slepichev, Alexander Efitorov, Aigul Dzhumamuratova, Viktor Kuznetsov, Hesam Rabeti, Joydeep Biswas, Soha Pouya
概要
CuVSLAMは、さまざまな視覚慣性センサーの組み合わせ（複数のRGBおよび深さカメラ、慣性測定装置を含む）を使用する最先端の視覚的同時位置認識およびマップ作成（Visual Simultaneous Localization and Mapping、VSLAM）ソリューションです。最小1台から最大32台のRGBカメラを任意の幾何学的構成でサポートし、さまざまなロボット設定に適用できます。 NVIDIA Jetsonなどのエッジコンピューティングデバイスで最小限の計算オーバーヘッドでリアルタイムアプリケーションにデプロイできるようにCUDAを使用して最適化されています。本稿では、cuVSLAMの設計と実装、ユースケース、および最先端のベンチマークに関する実験結果を示し、最高レベルのパフォーマンスを示しています。
Takeaways、Limitations
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Takeaways：
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さまざまな視覚慣性センサーの組み合わせをサポートする柔軟なVSLAMシステムを提供します。
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エッジコンピューティングデバイスでのリアルタイム駆動のためのCUDA最適化による効率的な計算
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複数のベンチマークで最高の性能を証明。
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さまざまなロボットプラットフォームに適用可能性を拡大
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Limitations：
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本論文では具体的なLimitationsについては言及しない。追加の実験と分析により、さまざまな環境や条件での性能限界を特定する必要があります。
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特定のセンサーまたは環境に依存するかどうかに関する追加情報が必要です。
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ソースコードが公開されているかどうか、およびスケーラビリティに関する情報が不足しています。
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