IntellWe的动态

Exploring the strengths of ORB, SIFT, and FREAK for image alignment. Dive into my latest Medium post for insights into these algorithms and how they can improve your workflows. #ImageProcessing #ComputerVision #DocumentProcessing #MachineLearning #DataScience #AI #AlgorithmAnalysis #TechInnovation

These AI visualization by websim and artifact is interesting Monte Carlo Simulation: Rolling a 6-sided Die https://lnkd.in/eFiep-KH Monte Carlo Dice Simulator https://lnkd.in/et5YWvR4 GeoTimeline: Interactive Geological Time Scale Explorer https://lnkd.in/e5MPCrvV Dijkstra's Algorithm Visualization https://lnkd.in/eFXeFaGP

Pixtral Large, a 124B open-weights multimodal model built on top of Mistral Large 2, demonstrates frontier-level image understanding. The model is particularly able to understand documents, charts, and natural images while maintaining Mistral Large 2's leading text-only understanding. https://lnkd.in/g9V4MSvK #genai #multimodal

Checkout the Supervision Cheatsheet created by Linas Kondrackis Link : https://lnkd.in/drQ7Rhzn #Roboflow #AI #ML

?? ?? ?? DepthSplat reconstructs 3D scenes from just a few images by combining Gaussian splatting with depth estimation. ?? More here ?? https://lnkd.in/gwRcgfSZ #aitool #airesearch #generativeai reposting from #x user #dreamingtulpa

VEnhancer: A Generative Space-Time Enhancement Method for Video Generation Recent advancements in video generation have been driven by large models trained on extensive datasets, employing techniques like adding layers to existing models and joint training. Some approaches use multi-stage processes, combining base models w... https://lnkd.in/e3vvGZdn #AI #ML #Automation

Did you miss our latest webinar? Catch up now on how to use Path Explorer AI to find preliminary optimized routes between two points, A & B. Ideal for greenfield road and corridor projects, Path Explorer saves upfront time in locating constructible candidate solutions. Key Features: ?Minimizes earthwork and surfacing costs ?Meets constraints like max grade, max side slope, min cut / max fill, no-go zones, high-cost zones ?Optimized Alignment options in minutes Watch it now! https://lnkd.in/gNGEwRdf - - - #RoadEng #PathExplorer #AI #RoadDesign #Webinar

Detecting hallucinations in diffusion models, especially in an inverse problem setting is extremely tricky. There are just a few works that tackle hallucination detection. The std process in natural image generation (txt2img models) is to rank the generations with a CLIP model acting as a judge. (in case of inverse problems rank with the misfit value is possible, but is a faulty metric) This one below is one which in my experience holds promise , but needs a fair bit of moulding to adopt it for geophysicsal inverse problem. Good to have this approach in your back pocket though. https://lnkd.in/e5xXzc95)

LinkedIn is buzzing with posts about AI-powered #fault extraction. But have you ever wondered what goes on behind the scenes? How much time and effort does it take to achieve those results? So we decided to record a video showing the whole process with Seisnetics. Not much to see though, just 2 clicks ?? The fault module in Seisnetics uses the horizons extracted automatically and without user input at the first step to build polygons, then planes. Originally, our video was over 52 minutes long but we've sped things up four times to save yours. Even though there might not be much to see most of the time, we wanted to give you a glimpse of the process. So, if you watch our video, don't be surprised if there are long stretches where not much seems to be happening (nothing from 10s to 6 min, then nothing from 6min50 to 12 minutes). We'll shoot another video soon to take the time to look at the results :) Our seismic data comes from the Gulf of Mexico (USGS website), near Texas, and it's a 20GB in size and processed with a Dell XPS9515 laptop, 64 GoRAM. It's part of the ongoing investigation into CCS initiatives. #Enjoy #CCS #3D #AI #faults

Introducing the Janus Pro 7B model from DeepSeek, a significant advancement in the realm of AI, capable of both multi-modal understanding and image generation. The video begins with an overview of the model's capabilities, followed by insights from the technical paper that details its architecture and functionality. A key highlight is the discussion on vector quantization techniques, which are essential for optimizing the model's performance. Additionally, a demonstration using a Colab notebook showcases the Janus Pro model in action, illustrating its image generation and understanding capabilities. This model represents a notable step forward in AI technology, offering promising applications across various fields. #AI #DeepLearning #ImageGeneration #MachineLearning #Innovation ---------------------- Learn more here: https://lnkd.in/eCbbzMWA