#Vision Transformers

This project delivers an extensive review of Masked Image Modeling (MIM) and associated techniques in self-supervised representation learning, presenting them in their historical sequence of development. It covers essential topics such as MIM for Transformers, contrastive learning, and applications in various modalities. The analysis includes the progression of self-supervised learning across diverse modalities, underscoring its pivotal role since 2018 in areas like NLP and Computer Vision. Contributions and revisions from the community are welcomed, along with resources such as curated paper lists and formats for academic citations. This is an essential resource for researchers and enthusiasts exploring the developments and practical applications in MIM.

RepViT-SAM addresses computational challenges in mobile vision tasks by replacing conventional image encoders with advanced RepViT models, enhancing segmentation speed and efficiency on devices such as iPhones. With this approach, RepViT-SAM achieves impressive zero-shot transfer performance and up to ten times faster inference. Leveraging state-of-the-art ViT and CNN integrations, the RepViT family sets a new standard in lightweight model performance, boasting over 80% top-1 accuracy on ImageNet while maintaining low latency.

EasyCV is a PyTorch-based computer vision toolkit specializing in self-supervised learning and transformer architectures. It covers various tasks such as image classification, object detection, and pose estimation. The toolkit features state-of-the-art self-supervised learning algorithms, Vision Transformers, and supports extensive functionality and scalability. EasyCV facilitates multi-GPU and multi-worker training, with accelerated data processing using DALI and training optimizations through TorchAccelerator and fp16. Recent updates introduce models like YOLOX-PAI and STDC, enhancing its capabilities in segmentation and video recognition. It integrates with PAI-EAS for seamless online deployment and monitoring.

DINOv2 is designed for robust visual feature extraction using unsupervised learning on a dataset of 142 million images. Its features work effortlessly with simple classifiers like linear layers, performing well in diverse computer vision tasks without the need for fine-tuning. With its integration of registers in Vision Transformers, DINOv2 offers improved performance, showcasing the latest advancements in the field. Available in multiple configurations via PyTorch Hub, it supports applications in image classification, depth estimation, and semantic segmentation. Discover how DINOv2's pretrained models enhance visual feature robustness and versatility.

Discover the latest advances in Computer Vision and Pattern Recognition at CVPR 2024, featuring workshops, research papers, and challenges across emerging topics like Vision Transformers and multimodal interactions. With an impressive acceptance rate from over 11,000 submissions, this event highlights cutting-edge innovation and collaboration in algorithm development, data exploration, and practical applications.