https://github.com/ericcjoyce/consensus-driven-uncertainty
공식 코드Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
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Consensus-Driven Uncertainty for Robotic Grasping based on RGB Perception
VQVC+: One-Shot Voice Conversion by Vector Quantization and U-Net architecture
HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
EdgeRegNet: Edge Feature-based Multimodal Registration Network between Images and LiDAR Point Clouds
EMDB: The Electromagnetic Database of Global 3D Human Pose and Shape in the Wild
TempCLR: Reconstructing Hands via Time-Coherent Contrastive Learning
AvatarPoser: Articulated Full-Body Pose Tracking from Sparse Motion Sensing
Restore Anything Pipeline: Segment Anything Meets Image Restoration
Human-Guided Fair Classification for Natural Language Processing
Towards Human-Level 3D Relative Pose Estimation: Generalizable, Training-Free, with Single Reference
FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation
PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation
3D3L: Deep Learned 3D Keypoint Detection and Description for LiDARs
Leveraging Deep Visual Descriptors for Hierarchical Efficient Localization
Semi-automatic 3D Object Keypoint Annotation and Detection for the Masses
Cross-Resolution Flow Propagation for Foveated Video Super-Resolution
Improving Transformer-based Image Matching by Cascaded Capturing Spatially Informative Keypoints
LoFT: LoRA-fused Training Dataset Generation with Few-shot Guidance
Assessing interaction recovery of predicted protein-ligand poses
Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal Learning
Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation
A Graph Attention Spatio-temporal Convolutional Network for 3D Human Pose Estimation in Video
Animal Avatars: Reconstructing Animatable 3D Animals from Casual Videos
A Method for Animating Children's Drawings of the Human Figure
Learning Temporal Pose Estimation from Sparsely-Labeled Videos
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.