Glossary

0-9

0-shot learning 1-shot learning 2-stage detector 3D convolution 4D data 5G + AI 6DoF pose estimation 7D representation 8-bit quantization 9-layer network

A

AGI / Artificial General Intelligence Algorithm Artificial Intelligence (AI)Attention Autoencoder

B

Backpropagation Batch Normalization BERT Bias Boosting

C

Chatbot Classifier / Classification Clustering CNN / Convolutional Neural Network Cross-Validation

D

Data Augmentation Deep Learning Deepfake Deterministic Model Discriminative Model

E

Embedding Encoder Ensemble Learning Epoch Explainable AI (XAI)

F

Feature Extraction Fine-tuning Forward Propagation Foundation Model Fusion / Multimodal Fusion

G

GAN / Generative Adversarial Network Generative AI Gradient Descent Graph Neural Network (GNN)Grounding

H

Hallucination Heuristic Hidden Layer Hierarchical Model Hyperparameter

I

Imbalanced Data Instance / Sample Instruction tuning Intelligence Amplification / Augmentation Interpretability

J

JAX Jittering Joint Embedding JSONL / JSON-lines Juxtaposition

K

K-means Clustering K-Shot Learning Kernel Trick KL Divergence (Kullback–Leibler Divergence)Knowledge Distillation

L

Large Language Model (LLM)Latent Variable Learning Rate Loss Function LSTM / Long Short-Term Memory

M

Machine Learning (ML)Meta-learning Model Multi-head Attention Multimodal / Multimodality

N

Neural Network NLP / Natural Language Processing NLU / Natural Language Understanding Normalization Novelty Detection / Anomaly Detection

O

Objective Function One-hot Encoding Online Learning Optimizer Overfitting

P

Parameter Policy / Reinforcement Learning Policy Pooling Pretraining Prompt

Q

Q-learning Quality Estimation Quantization Query Queue / Buffer

R

Regularization Reinforcement Learning (RL)Representation Learning Retrieval Augmented Generation (RAG)RNN / Recurrent Neural Network

S

Sampling Self-Supervised Learning Sequence Modeling Softmax Supervised Learning

T

Tokenizer Training Data Transfer Learning Transformer Tuning / Hyperparameter Tuning

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

Validation Set Vanishing / Exploding Gradient Variational Autoencoder (VAE)Vector Embedding Vision Transformer (ViT)

W

Weak Supervision Weight Decay Whitening / Whitening Transformation Word Embedding Workflow

X

X-axis / feature axis XAI / Explainable AI XLM XLNet XOR problem

Y

Y-axis / feature axis Y-transform / YUV YAGNI (You Aren't Gonna Need It)Yield (model yield / throughput)Yoga of AI

Z

Z-score Normalization Zero-centric / Zero-bias initialization Zero-gradient phenomenon Zero-shot Learning / Zero-shot inference Zygosity in augmentation

What is 6DoF pose estimation

6DoF (six degrees of freedom) pose estimation is a crucial concept in computer vision and robotics that aims to determine the position and orientation of objects in 3D space. 6DoF refers to the three translational degrees of freedom (movement along X, Y, and Z axes) and three rotational degrees of freedom (rotation around X, Y, and Z axes). This technology plays a significant role in applications such as augmented reality (AR), virtual reality (VR), autonomous driving, drone navigation, and robotics.

The operation of pose estimation typically relies on sensor data (such as cameras and LiDAR) and machine learning algorithms to gather environmental information and infer the exact position and orientation of objects. Common algorithms include feature-based methods, deep learning approaches, and filtering techniques. These methods can be employed in various scenarios, including indoor localization, outdoor navigation, and motion capture.

The advantages of 6DoF pose estimation include improved accuracy in object recognition and tracking, enhancing user experience, especially in AR and VR applications. However, it also has drawbacks, such as sensitivity to environmental lighting changes, high computational resource requirements, and potential errors in complex scenes.

In the future, with advancements in deep learning technologies and improvements in hardware performance, 6DoF pose estimation is expected to become more precise and efficient. Moreover, multimodal approaches that integrate data from various sensors may further enhance its performance in dynamic environments.

Considerations include selecting appropriate algorithms and sensors, addressing real-time performance requirements, and coping with environmental variations.