Tech Xplore

Neuromorphic computer prototype learns patterns with fewer computations than traditional AI

Could computers ever learn more like humans do, without relying on artificial intelligence (AI) systems that must undergo ...
IEEE

Emerging Properties in Self-Supervised Vision Transformers

Abstract: In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) [16] that stand out compared to convolutional networks (convnets). Beyond the fact ...

Google's 'Watch & Learn' framework cracks the data bottleneck for training computer-use agents

The new framework solves AI's "data bottleneck" by automatically generating high-quality training examples from raw screen ...
Unite.AI

DINOv3 and the Future of Computer Vision: Self-Supervised Learning at Scale

Labeling images is a costly and slow process in many computer vision projects. It often introduces bias and reduces the ability to scale large datasets. Therefore, researchers have been looking for ...
GitHub

Self-Supervised Vision Transformers with DINO

PyTorch implementation and pretrained models for DINO. For details, see Emerging Properties in Self-Supervised Vision Transformers. Run DINO with ViT-small network on a single node with 8 GPUs for 100 ...
IEEE

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Abstract: This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language ...