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

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

B

BERT Boosting Backpropagation Batch Normalization Bias

C

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

D

Discriminative Model Deterministic Model Deep Learning Data Augmentation Deepfake

E

Epoch Encoder Ensemble Learning Explainable AI (XAI)Embedding

F

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

G

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

H

Hierarchical Model Hyperparameter Hallucination Heuristic Hidden Layer

I

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

J

JAX JSONL / JSON-lines Juxtaposition Jittering Joint Embedding

K

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

L

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

M

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

N

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

O

One-hot Encoding Overfitting Objective Function Optimizer Online Learning

P

Prompt Parameter Pretraining Policy / Reinforcement Learning Policy Pooling

Q

Q-learning Query Queue / Buffer Quantization Quality Estimation

R

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

S

Softmax Sampling Supervised Learning Sequence Modeling Self-Supervised Learning

T

Tokenizer Transformer Tuning / Hyperparameter Tuning Transfer Learning Training Data

U

U-Net Uncertainty Estimation Underfitting Universal Approximation Theorem Unsupervised Learning

V

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

W

Weight Decay Word Embedding Workflow Whitening / Whitening Transformation Weak Supervision

X

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

Y

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

Z

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

什么是6DoF姿态估计

6DoF（六自由度）姿态估计是计算机视觉和机器人领域中的一个重要概念，旨在确定物体在三维空间中的位置和方向。6DoF指的是物体在三维空间中的三个平移自由度（沿X、Y、Z轴的移动）和三个旋转自由度（绕X、Y、Z轴的旋转）。这种技术在增强现实（AR）、虚拟现实（VR）、自动驾驶、无人机导航和机器人等应用中具有重要意义。

姿态估计的运作方式通常依赖于传感器数据（如相机、激光雷达等）和机器学习算法，以获取环境信息并推断出物体的确切位置和方向。常见的算法包括基于特征点的方法、深度学习方法和滤波算法等。这些方法可以在不同的场景下使用，如室内定位、外部导航和运动捕捉等。

6DoF姿态估计的优点包括提高了物体识别和跟踪的准确性，增强了用户体验，尤其在AR和VR应用中。然而，它也存在一些缺点，如对环境光照变化的敏感性、对计算资源的高需求以及在复杂场景下可能出现的错误。

在未来，随着深度学习技术的进步和硬件性能的提升，6DoF姿态估计预计会变得更加精确和高效。此外，结合多种传感器数据的多模态方法可能会进一步提高其在动态环境中的表现。

相关注意事项包括选择合适的算法和传感器、考虑实时性需求以及应对环境变化的能力。