DeepSeek AI released DeepSeek-OCR 2, an open source document OCR and understanding system that restructures its vision encoder to read pages in a causal order that is closer to how humans scan complex documents. The key component is DeepEncoder V2, a language model style transformer that converts a 2D page into a 1D sequence of visual tokens that already follow a learned reading flow before text decoding starts. https://github.com/deepseek-ai/DeepSeek-OCR-2 From raster order to causal visual flow Most multimodal models still flatten images into a fixed raster sequence, top left to bottom right, and apply a transformer with static positional encodings.…

We implement an advanced, end-to-end Kornia tutorial and demonstrate how modern, differentiable computer vision can be built entirely in PyTorch. We start by constructing GPU-accelerated, synchronized augmentation pipelines for images, masks, and keypoints, then move into differentiable geometry by optimizing a homography directly through gradient descent. We also show how learned feature matching with LoFTR integrates with Kornia’s RANSAC to estimate robust homographies and produce a simple stitched output, even under constrained or offline-safe conditions. Finally, we ground these ideas in practice by training a lightweight CNN on CIFAR-10 using Kornia’s GPU augmentations, highlighting how research-grade vision pipelines translate naturally…