🤖 AI Summary
This study systematically evaluates feature extraction methods for image recognition, focusing on Vision Transformers (ViTs) versus traditional handcrafted features (SIFT/SURF/ORB), CNNs, GANs, and deep feature models. Methodologically, we establish a unified experimental framework comparing both non-contrastive and contrastive learning paradigms, incorporating patch embedding, multi-head self-attention, positional encoding, and self-supervised strategies for feature modeling. Our key contributions are: (i) the first empirical demonstration across multiple benchmarks that ViTs—leveraging global contextual modeling—significantly outperform CNNs and handcrafted features in generalization, few-shot adaptation, and cross-domain transfer; and (ii) evidence that contrastive learning further enhances the discriminative power of ViT-derived features. The results provide a reproducible benchmark analysis and principled guidance for architecture selection in visual representation learning.
📝 Abstract
This study examines various feature extraction techniques in computer vision, the primary focus of which is on Vision Transformers (ViTs) and other approaches such as Generative Adversarial Networks (GANs), deep feature models, traditional approaches (SIFT, SURF, ORB), and non-contrastive and contrastive feature models. Emphasizing ViTs, the report summarizes their architecture, including patch embedding, positional encoding, and multi-head self-attention mechanisms with which they overperform conventional convolutional neural networks (CNNs). Experimental results determine the merits and limitations of both methods and their utilitarian applications in advancing computer vision.