Computer Vision (CV) large models have revolutionized the field with their ability to process and analyze vast amounts of visual data. These models are often referred to by a variety of names in English, each reflecting different aspects of their design, functionality, or the specific tasks they are designed to perform. Below is a comprehensive guide to some of the common names used for CV large models in English.
1. General Terminology
1.1 Deep Learning Models
Deep learning is a subset of machine learning that involves neural networks with many layers. CV large models are often deep learning models due to their complexity and depth.
- Neural Network: A series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
- Convolutional Neural Network (CNN): A class of deep neural networks, most commonly applied to analyzing visual imagery.
1.2 Large Models
The term “large” refers to the size of the model, which is typically measured by the number of parameters and the amount of data required for training.
- Large Language Model (LLM): While not specific to CV, this term is used to describe models that have been trained on a massive corpus of text data.
- Vision Transformer (ViT): A CNN architecture that uses self-attention mechanisms, originally designed for image classification tasks.
2. Specific Model Names
2.1 Image Recognition and Classification
- ResNet (Residual Network): An architecture with skip connections to improve the training of deep neural networks.
- Inception (GoogLeNet): An architecture that uses a series of parallel convolutions to extract features at different scales.
- MobileNet: A family of neural network architectures designed for mobile and edge devices.
2.2 Object Detection
- Faster R-CNN: A popular object detection framework that combines region proposal with convolutional neural networks.
- YOLO (You Only Look Once): An object detection system that processes an image in a single forward pass.
- SSD (Single Shot MultiBox Detector): A fast and accurate object detector that uses a single deep neural network to perform the detection.
2.3 Semantic Segmentation
- FCN (Fully Convolutional Network): An architecture that allows for pixel-wise classification.
- U-Net: An architecture specifically designed for biomedical image segmentation.
2.4 3D Vision
- PointNet: A deep learning architecture that can be used for 3D point cloud processing.
- PointNet++: An extension of PointNet that introduces a multi-scale processing idea.
2.5 Generative Models
- GAN (Generative Adversarial Network): A class of neural networks that seek to generate new data with similar statistics to real-world data.
- StyleGAN: A variant of GAN that can generate images with high fidelity to the input images.
3. Model Variants and Specializations
3.1 Variants
- EfficientNet: A set of architectures that scale the depth, width, and resolution of networks in a way that is computationally efficient.
- DenseNet: An architecture that connects each layer to every other layer in a feedforward fashion.
3.2 Specializations
- Domain-Specific Models: Models designed for specific tasks within CV, such as medical image analysis, autonomous driving, or facial recognition.
4. Conclusion
The field of CV large models is vast and continually evolving. New models and architectures are being proposed regularly, each with its own unique features and applications. Keeping up with the latest developments in this area is crucial for those working in the field of computer vision.