Transformer-Driven Self-Supervised Learning for Visual Understanding: Methods and Applications

With the rise of self-attention mechanisms, Transformer architectures have demonstrated remarkable capability in modeling long-range dependencies, gradually establishing their influence across critical domains in computer vision, including self-supervised learning, image classification, object detection, image generation, and super-resolution reconstruction. Under the self-supervised learning paradigm, Transformer-based approaches—leveraging contrastive learning and masked reconstruction—have extracted highly transferable visual representations from unlabeled data. Vision Transformer (ViT) and its hybrid convolution—attention variants have surpassed traditional convolutional neural networks (CNNs) on large-scale classification benchmarks. Detection Transformer (DETR) and its sparse or deformable derivatives have streamlined detection pipelines while improving small-object recognition. In the realm of generative modeling, Image Transformer and MaskGIT adopt autoregressive pixel prediction and masked visual token modeling respectively, enabling high-fidelity image synthesis and accelerated decoding. For image super-resolution, models like TTSR and HAT exploit texture transfer and hybrid attention strategies to restore convincing high-resolution details under extreme upscaling. The convergence of global attention modeling and localized feature extraction not only highlights the complementarity between CNNs and Transformers but also paves the way for future advances in multimodal pretraining, dynamic attention routing, and lightweight deployment.