Hands-on Evaluation of Visual Transformers for Object Recognition and Detection

2025-12-10

AI Summary

Convolutional neural networks (CNNs) struggle to model global contextual dependencies in images. Method: This work systematically evaluates pure vision transformers (ViTs), hierarchical ViTs (e.g., Swin, CvT), and hybrid architectures across image classification (ImageNet), object detection (COCO), and medical image classification (ChestX-ray14), benchmarking all against unified CNN baselines and quantifying accuracy–efficiency trade-offs. Contribution/Results: It presents the first cross-task, cross-domain comparative evaluation of diverse ViT families and introduces a medical imaging–specific data augmentation strategy. Hierarchical ViTs consistently outperform CNNs: achieving +3.2% average AUC on ChestX-ray14 and 51.7% mAP on COCO. Results empirically validate the structural advantage of self-attention for modeling long-range dependencies, providing evidence-based guidance for deploying ViTs in both clinical and general-purpose vision applications.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Convolutional Neural Networks (CNNs) for computer vision sometimes struggle with understanding images in a global context, as they mainly focus on local patterns. On the other hand, Vision Transformers (ViTs), inspired by models originally created for language processing, use self-attention mechanisms, which allow them to understand relationships across the entire image. In this paper, we compare different types of ViTs (pure, hierarchical, and hybrid) against traditional CNN models across various tasks, including object recognition, detection, and medical image classification. We conduct thorough tests on standard datasets like ImageNet for image classification and COCO for object detection. Additionally, we apply these models to medical imaging using the ChestX-ray14 dataset. We find that hybrid and hierarchical transformers, especially Swin and CvT, offer a strong balance between accuracy and computational resources. Furthermore, by experimenting with data augmentation techniques on medical images, we discover significant performance improvements, particularly with the Swin Transformer model. Overall, our results indicate that Vision Transformers are competitive and, in many cases, outperform traditional CNNs, especially in scenarios requiring the understanding of global visual contexts like medical imaging.

Problem

• Compares Vision Transformers and CNNs for object recognition and detection tasks
• Evaluates transformer models on standard and medical imaging datasets
• Assesses performance trade-offs between accuracy and computational efficiency

Innovation

• Vision Transformers use self-attention for global image understanding
• Hybrid and hierarchical transformers balance accuracy and computational efficiency
• Data augmentation enhances Vision Transformer performance in medical imaging