- 25-07-2025
- Computer Vision
Recent AI advances in computer vision empower machines with human-like perception using transformers, self-supervised learning, and 3D modeling.
Computer vision is experiencing a revolution fueled by cutting-edge developments in deep learning and AI. Transformer-based architectures like Vision Transformer (ViT) and Swin Transformer have redefined how machines interpret images, surpassing traditional CNNs in tasks such as object detection and scene segmentation. Meanwhile, self-supervised learning methods like DINO, SimCLR, and MoCo eliminate the need for extensive labeled datasets, unlocking vast potential in unannotated data.
The integration of visual and linguistic modalities through models like CLIP and ALIGN allows machines to reason across images and language, enabling zero-shot classification and complex understanding. Alongside, breakthroughs in 3D modeling—such as Neural Radiance Fields (NeRF)—and edge-optimized models like MobileViT are making real-time, photorealistic, and resource-efficient vision systems a reality. These advancements are shaping the future of AI in fields like robotics, AR/VR, autonomous navigation, and beyond.