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 Fusion / Multimodal Fusion

Fusion refers to the combination of different elements or technologies into a new whole. In computer science and artificial intelligence, Multimodal Fusion refers to integrating data from multiple modalities (such as text, images, audio, etc.) for more comprehensive and accurate analysis and understanding.

The importance of Multimodal Fusion is increasing as the diversity of data sources and forms grows. It enhances the performance of machine learning models, especially in tasks requiring comprehensive analysis of different data types, such as autonomous driving and sentiment analysis. By integrating multimodal information, systems can make more precise judgments in complex scenarios.

Typically, Multimodal Fusion involves three steps: data preprocessing, feature extraction, and fusion strategy. Data preprocessing involves cleaning and standardizing different modalities; feature extraction captures useful information from each modality; and the fusion strategy determines how to combine this information (e.g., through weighted averages or deep learning models).

In medical imaging analysis, Multimodal Fusion can combine CT images and MRI data to provide more comprehensive diagnostic information. In natural language processing, the combination of text and images can help improve the accuracy of image caption generation.

Looking ahead, as AI technology continues to develop, Multimodal Fusion will be applied in more fields such as virtual reality, augmented reality, and human-computer interaction. Additionally, as data volumes increase, efficiently processing and fusing this data will become an important research direction.

Benefits include more comprehensive data analysis and improved model accuracy and robustness, while drawbacks include processing complexity and higher computational costs.

When implementing Multimodal Fusion, attention must be paid to the quality, scale, and temporal synchronization of different modality data, as these factors can affect the accuracy of the final results.