Computer vision, a field within artificial intelligence (AI), enables machines to interpret and understand visual data from images and videos. This technology allows AI systems to extract meaningful information, such as detecting objects, analyzing scenes, or recognizing faces. It plays a role in numerous real-world applications, from autonomous vehicles recognizing traffic signs to medical imaging systems identifying diseases. Advancements in computer vision are continuously transforming various industries.
Understanding Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are a type of deep learning algorithm designed for processing visual data. They are structured with an input layer, an output layer, and multiple hidden layers that include convolutional, pooling, and fully connected layers. CNNs process images by applying filters, also known as kernels, to automatically detect and extract features like edges, shapes, and textures from local regions.
The convolutional layers are at the core of a CNN, where filters slide over the input image, performing dot products to create feature maps. These maps represent patterns in the image. Activation functions like ReLU then introduce non-linearity, enabling the network to learn complex relationships. Pooling layers reduce the spatial dimensions of these feature maps, simplifying data while retaining important information. Finally, fully connected layers use the processed features to make predictions, such as classifying an image.
Understanding Vision Transformers
Vision Transformers (ViTs) represent an adaptation of the Transformer architecture, originally developed for natural language processing (NLP) tasks. The Transformer architecture, introduced in 2017, revolutionized NLP by using a self-attention mechanism to process sequences of data, allowing models to understand context and relationships between different parts of a sequence.
To adapt this for images, ViTs first divide an image into fixed-size, non-overlapping patches, typically 16×16 pixels. Each patch is then flattened and transformed into a linear embedding, acting like a “token” similar to a word in NLP. Positional embeddings are added to these patch embeddings to preserve spatial information, as the self-attention mechanism does not inherently account for position. These embedded patches are then fed into a standard Transformer encoder, where the self-attention mechanism allows each patch to interact with every other patch, capturing global relationships across the entire image.
How They Differ Architecturally
CNNs and Vision Transformers approach image processing with fundamental architectural differences, particularly in their inductive biases and how they handle global versus local information. CNNs possess strong inductive biases, meaning they have built-in assumptions about image data. These biases include locality, which assumes nearby pixels are related, and translation equivariance, where features can be recognized regardless of their position due to weight sharing across the image. This allows CNNs to learn effectively even with moderate amounts of training data.
Vision Transformers, in contrast, have minimal inductive biases. They do not inherently assume local relationships or translation equivariance, instead relying on the self-attention mechanism to learn these relationships directly from the data. This means ViTs often necessitate much larger datasets—tens of millions of examples or more—to achieve comparable or superior performance to CNNs.
Regarding processing, CNNs extract features hierarchically by applying local convolutional filters that scan small regions of an image. They build a global understanding gradually, layer by layer, from simple features like edges to more complex shapes. ViTs, on the other hand, immediately capture global relationships from the first layer through their self-attention mechanism. Each patch can attend to all other patches, regardless of their spatial distance, allowing for a more holistic understanding of the image from the outset.
In terms of scalability, ViTs are highly scalable and can outperform CNNs when trained on very large datasets. However, the self-attention mechanism in ViTs has a quadratic computational complexity with respect to the number of image patches, which can make them computationally intensive, especially for high-resolution images. CNNs are generally more computationally efficient for many tasks, as their convolutional operations are optimized for image data. While CNNs may not scale as well as ViTs with increasing data, they often perform better in scenarios with smaller pretraining datasets.
Strengths, Weaknesses, and Use Cases
CNNs exhibit several strengths, including their robustness with smaller datasets due to their inherent inductive biases. They are computationally efficient, making them practical for real-time applications and environments with limited resources. CNNs also benefit from decades of refinement, with many established architectures and pre-trained models available. For instance, CNNs are widely used in medical imaging for tasks like tumor detection and disease diagnosis, especially where annotated data might be limited.
CNNs’ reliance on local receptive fields can limit their ability to capture long-range dependencies or global context within an image. They can also be prone to overfitting if training data is limited or noisy.
ViTs offer distinct advantages, particularly their ability to capture global context and long-range dependencies across an entire image. This makes them effective for tasks where understanding the overall scene or relationships between distant objects is important. ViTs also demonstrate strong performance and potential for better generalization when trained on massive datasets. They are increasingly used in large-scale image classification, object detection, image generation, and medical imaging, especially when large datasets are available.
ViTs are “data-hungry,” often requiring extensive datasets for optimal performance. Without sufficient data, ViTs can overfit and perform poorly compared to CNNs. Their self-attention mechanism also leads to higher computational costs and greater memory requirements, which can be a limiting factor for resource-constrained applications.