Transformers for Vision: A Game-Changer for Computer Vision Models?

In the context of the computer vision field, there have been significant recent developments, of which the transformer models are among the most significant. Transformer was first introduced for natural language processing (NLP) but has demonstrated immense capacity for vision tasks and has already risen to compete with the most dominant network, the convolutional neural networks (CNNs). This is the fourth blogpost of the series Blogpost series on how cutting-edge AI paradigms are being adapted and extended into computer vision So in this blog-post we will be – Understanding how transformers are transforming computer vision – Discussions and analysis of the main features that are driving it – How they are enabling the evolution of more effective models of visual processing

Introduction to Transformers

Transformers were originally proposed at the end of 2017 in the paper by Vaswani et al, the researchers of Google Brain. This new structure of architecture assists in discovering dependency between the parts of the sequence without involving the recurrent or convolutional structures which reduces the amount of language data it processes.

In NLP, transformers set the new trend for the model architecture, and the BERT and GPT models that were derived from the transformer architecture achieved benchmarks across multiple NLP tasks. Clearly, its success in NLP sparked the interest of computer vision researchers to attempt to extend this architecture to vision tasks even though it initially appeared to struggle with images.

Why Transformers in Vision?
Until recently, CNNs were by far the most widely used architecture for computer vision because through the convolution layers they are able to encapsulate spatial information from images. Nevertheless, CNNs are not perfect. They perform poorly on image structures that exist over large spatial distances and as models become denser and larger, they entitle more data and computational power. They, with their attention mechanisms, offer a different approach that has some key advantages:

Global Context: Transformers can preserve relations within an image, rather than only within its local windows typical for CNNs.

Parallelization: In contrast, the transformers architecture facilitates the parallel computation of all tokens within a sequence which could be faster to optimize and faster to train than sequential computations.

Vision Transformers (ViT): The Breakthrough Model
The Vision Transformer (ViT), proposed by researchers affiliated with Google Research in late of 2020, is a model forcing the transformer architecture over the patches of an image, as opposed to applying it over words. Here’s how it works:

• Image Patching: ViT splits an input image into patches of fixed sizes (for instance, 16×16 pixels) therefore mapping the input image into sequence of smaller tokens.
  Patch Embedding: They are then flattened and transformed into the vector embeddings, as if words are in NLP models, for each patch.
• Positional Encoding: ViT further feeds positional encodings into the patch embeddings because transformers do not inherently have an order.
• Self-Attention Layers: With the architecture of the transformer, the interactions between the patches are modeled using self-attention layers which also make it possible for the model to capture specific relations within the whole image.
  In ViT, the authors proved that a transformer architecture can perform well on image classification tasks without convolutions. For instance, ViT outperformed other architectures such as CNNs on benchmarks such as ImageNet with reduced parameters as long as large datasets were employed in ViT training.

                                                          Fig.1 Depicting the functioning of vision transformers.     

Advantages of Vision Transformers Over CNNs
Nonetheless, CNNs are popular still now, but ViTs revealed several perks that indicate about them as a potential and worthy substitute, particularly when dataset and computational resources expand.

• Enhanced Long-Range Dependency Modeling: The self-attention of Transformers can capture long distance dependencies across the image while also capturing local dependencies.
• Parameter Efficiency with Scaling: In the large dataset, Vision transformers when scaled, need far less fine-tuning than CNNs and are more parameter-efficient for large datasets.
• Better Performance on Data-Hungry Tasks: Such a case is true for large datasets for which ViTs are well-suited due to their performance on such tasks like video analysis.
  Generalization Across Modalities: Another advantage of transformers concerning their flexibility, is that transformers are not tied to a certain kind of data: it can be an image, text or even a multimodal data.

Key Challenges in Adopting Vision Transformers

While ViTs are promising, they do face some significant challenges:

• Data Requirements: ViTs require vast amounts of labeled data to achieve competitive performance, limiting their utility for applications with smaller datasets.
• Computational Cost: Despite being scalable, training transformers is computationally expensive, often requiring large-scale infrastructure not accessible to all.
• Overfitting: ViTs are more prone to overfitting on small datasets, which can result in poor generalization performance without proper regularization or data augmentation.

To overcome these challenges, researchers have developed variations of ViTs and hybrid models that combine the strengths of transformers and CNNs. Examples include Data-efficient Image Transformers (DeiT), which use data augmentation techniques, and CNN-transformer hybrids that combine convolutional layers with self-attention layers to create more versatile architectures.

Applications and Real-World Use Cases of Vision Transformers

Vision transformers have opened new opportunities for computer vision applications across multiple domains:

1. Image Classification: ViTs have already demonstrated strong performance on classification tasks, often surpassing CNNs on benchmarks like ImageNet when trained on large datasets.
2. Object Detection and Segmentation: Models like DETR (Detection Transformer) have applied transformers to object detection with impressive results, capturing complex spatial relationships that CNN-based methods sometimes struggle with.
3. Medical Imaging: Transformers are particularly valuable in medical image analysis for tasks that require understanding long-range dependencies, like tissue segmentation and multi-organ analysis.
4. Video Processing: Transformers are also finding applications in video processing tasks where long-range dependencies over time and space are critical, such as action recognition and video summarization.

Hybrid Models: CNNs and Transformers together
Since the CNNs’ capability and transformer’s efficacy have been established, the integration of both has become a common technique to reap the benefit of both. Most of these models employ a couple of convolutional layers at the start to learn local features and are followed by transformer layers to learn relationships between other elements in a sequence.

Examples of such hybrid architectures include:

• CNN-Transformer Hybrid Models: One type of models is based on CNN for the initial feature extraction in combination with the transformers responsible for global relations.
• Convolution-Augmented Transformers: A set of models that extend ViTs with convolutional layers in order to increase the data efficiency on relatively small datasets.
  This technique has produced improvements to models like Swin Transformers that demonstrate that CNN and transformers in tandem can achieve SOTA performance particularly in densely predicting tasks such as object detection and segmentation.

Future Prospects: Beyond Vision Transformers
Vision transformers have become the go-to model in many computer vision tasks, but it has barely begun. Future advancements are likely to focus on:

• Improving Data Efficiency: Others include semi-supervised learning, and synthetic data generation as possible ways of minimizing ViTs dependency on labeled data.
• Energy-Efficient Transformer: Investigating the possible methods of decreasing the complexity of calculators required to operate with transformer, that is, the utilization of methods of quantization, pruning, or constructing hardware-friendly structures, can extend the applicability of transformer.
• Multi-Modal Transformer: Transformers which can take input from different domains like image, text and audio are likely to be the next generation, to build better and complex applications involving the use of artificial intelligence like self driving cars and related facilities.

Conclusion: A New Era for Computer Vision Models

Transformers have disrupted the status quo in computer vision, offering an architecture that captures complex, long-range relationships that CNNs often miss. Vision Transformer (ViTs) and their hybrid derivatives represent a significant step forward, especially as large datasets and compute power become more available. While there are still challenges to overcome, such as data requirements and computational costs, the momentum behind transformer suggests they will continue to redefine the possibilities in computer vision.

For more blogs, visit here.