Search Results for author:
Found 10 papers, 7 papers with code
TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models
The performance of testing newly incoming unseen time series on current TSAD algorithms remains unknown.
Revisiting VAE for Unsupervised Time Series Anomaly Detection: A Frequency Perspective
To ensure an accurate AD, FCVAE exploits an innovative approach to concurrently integrate both the global and local frequency features into the condition of Conditional Variational Autoencoder (CVAE) to significantly increase the accuracy of reconstructing the normal data.
Forging Vision Foundation Models for Autonomous Driving: Challenges, Methodologies, and Opportunities
The rise of large foundation models, trained on extensive datasets, is revolutionizing the field of AI.
RadOcc: Learning Cross-Modality Occupancy Knowledge through Rendering Assisted Distillation
3D occupancy prediction is an emerging task that aims to estimate the occupancy states and semantics of 3D scenes using multi-view images.
GSmoothFace: Generalized Smooth Talking Face Generation via Fine Grained 3D Face Guidance
Our proposed GSmoothFace model mainly consists of the Audio to Expression Prediction (A2EP) module and the Target Adaptive Face Translation (TAFT) module.
OpsEval: A Comprehensive IT Operations Benchmark Suite for Large Language Models
Information Technology (IT) Operations (Ops), particularly Artificial Intelligence for IT Operations (AIOps), is the guarantee for maintaining the orderly and stable operation of existing information systems.
Beyond Sharing: Conflict-Aware Multivariate Time Series Anomaly Detection
Massive key performance indicators (KPIs) are monitored as multivariate time series data (MTS) to ensure the reliability of the software applications and service system.
SR-OOD: Out-of-Distribution Detection via Sample Repairing
Specifically, our framework consists of two components: a sample repairing module and a detection module.
Generative Adversarial Network
Out-of-Distribution Detection
+1
An Effective Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
Due to the motion-centric nature, our method shows its impressive generalizability with limited training labels and provides good differentiability for end-to-end cycle training.
Beyond 3D Siamese Tracking: A Motion-Centric Paradigm for 3D Single Object Tracking in Point Clouds
3D single object tracking (3D SOT) in LiDAR point clouds plays a crucial role in autonomous driving.
Ranked #1 on
Object Tracking
on KITTI
