Glossary

0-9

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

A

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

B

BERT Boosting Bias Backpropagation Batch Normalization

C

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

D

Deepfake Discriminative Model Data Augmentation Deep Learning Deterministic Model

E

Epoch Explainable AI (XAI)Embedding Encoder Ensemble Learning

F

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

G

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

H

Hierarchical Model Hallucination Heuristic Hyperparameter Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Optimizer Objective Function Online Learning Overfitting

P

Prompt Pooling Parameter Policy / Reinforcement Learning Policy Pretraining

Q

Q-learning Quality Estimation Query Quantization Queue / Buffer

R

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

S

Softmax Sampling Sequence Modeling Supervised Learning Self-Supervised Learning

T

Tokenizer Transformer Training Data Tuning / Hyperparameter Tuning Transfer Learning

U

U-Net Uncertainty Estimation Underfitting Unsupervised Learning Universal Approximation Theorem

V

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

W

Weight Decay Workflow Weak Supervision Whitening / Whitening Transformation Word Embedding

X

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

Y

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

Z

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

什麼是6DoF姿態估計

6DoF（六自由度）姿態估計是計算機視覺和機器人領域中的一個重要概念，旨在確定物體在三維空間中的位置和方向。6DoF指的是物體在三維空間中的三個平移自由度（沿X、Y、Z軸的移動）和三個旋轉自由度（繞X、Y、Z軸的旋轉）。這種技術在增強現實（AR）、虛擬現實（VR）、自動駕駛、無人機導航和機器人等應用中具有重要意義。

姿態估計的運作方式通常依賴於傳感器數據（如相機、激光雷達等）和機器學習算法，以獲取環境信息並推斷出物體的確切位置和方向。常見的算法包括基於特徵點的方法、深度學習方法和濾波算法等。這些方法可以在不同的場景下使用，如室內定位、外部導航和運動捕捉等。

6DoF姿態估計的優點包括提高了物體識別和跟蹤的準確性，增強了用戶體驗，尤其在AR和VR應用中。然而，它也存在一些缺點，如對環境光照變化的敏感性、對計算資源的高需求以及在複雜場景下可能出現的錯誤。

在未來，隨著深度學習技術的進步和硬體性能的提升，6DoF姿態估計預計會變得更加精確和高效。此外，結合多種傳感器數據的多模態方法可能會進一步提高其在動態環境中的表現。

相關注意事項包括選擇合適的算法和傳感器、考慮即時性需求以及應對環境變化的能力。