与「computervision」相关的搜索结果

从疫情期间我就我反反复复一直讲，飞书和钉钉这类办公SaaS有三个死穴， 一个是中国人太穷，人均月薪4000，飞书和钉钉订阅费比他妈买条人命都贵，企业不可能给这种SaaS付钱， 一个是中国人只会用手机，中国人普遍18岁以前没接触过电脑，对电脑使用熟练度和技巧低于美国小学生，约等于一个大猩猩， 一个是哪怕工作后，中国人也严重依赖于office套装，哪怕在本地用，一遍遍发邮件和微信群传文件，反复折腾，反复传文件，反复保存，反复边传文件边扯皮，来来回回三四十次，也打死不愿意用协同办公SaaS。 在中国人只有1/4的人口有自己的电脑、中国人人均电脑使用水平不如一只野生大猩猩和边牧的前提下，想推广飞书和钉钉这类工具，最大的门槛在于培养用户的使用习惯，越早越好。 培养用户使用习惯的根源，在于让大学生全部用上，使用四年的时间，深度绑定一款移动办公SaaS，这样当他们工作以后，才会彻底绑死这个工具。 这件事在中国是有案例的，当年freescale作为摩托罗拉半导体拆分出来的公司，在中国最成功的策略，就是狠狠投资freescale高性能单片机为核心的智能车比赛， 导致中国整整两代大学生对freescale芯片比intel还熟悉，像我这一代的人，都经历过拿着冈萨雷斯computer vision的书在freescale极低算力下强行写binary segmentation算法，手写PID，手写电机控制，手写一堆复杂策略等等的一辆完整自动寻轨迹的智能车，且全程记事本手写，难度远高于宇树科技的99%的工作。 如果放在移动办公领域，我个人的建议是，飞书和钉钉应该绑死大学生群体，彻底给他们免费，培养他们四年的使用习惯。 如果你不给他们免费，他们也只能免费用，还必须白嫖，开会40分钟还要退出重进，每次退出重进一次，还要骂你马云和张一鸣全家王八蛋； 如果你给他们彻底免费了，他们至少本科四年彻底绑死一款产品，就有很大概率之后工作中把这套东西彻底带到公司里，甚至自己成立公司后彻底指定绑死其中一家的产品。 你用本科四年的免费账号，凭借学生邮箱，绑死1TB存储空间和无限会议，学生们会拿你们的工具当宝贝天天用，用了四年的时间，培养出习惯，就彻底不用office了。 这点道理想不明白，这俩项目可以说白活白干，不懂点基本的人事儿。

15 AI related accounts you should follow on Twitter: 1. @karpathy His tweets already create LLMs narratives that you later see on linkedin in 2 months. 2. @fchollet posts thoughtful research on intelligence, benchmarks, and AI limitations. Keras creator + ARC-AGI 3. @ylecun Yann LeCun is Deep learning pioneer & Meta Chief AI Scientist; big-picture research takes and critiques (and drama). 4. @AndrewYNg Andrew Ng is AI education legend; practical ML advice, courses, and real-world implementation. creator of deeplearning ai 5. @rasbt Sebastian Raschka posts on Practical ML/LLM implementations, "build from scratch" tutorials, and books. 6. @dair_ai Weekly ML/AI paper threads and accessible research explainers (high-signal for staying current). 7. @lilianweng Lilian Weng is ex-OpenAI, and her Lil'Log-style threads are good. has In-depth LLM research breakdowns 8. @jeremyphoward posts interesting takes on AI/crypto news, and works on democratizing practical deep learning and accessible education. 9. @simonw Simon posts Practical LLM tools, takes, experiments, prompting, and engineering breakdowns. django co-founder 10. @_akhaliq Curates the latest arXiv papers, model releases, and open-source AI drops. 11. @ID_AA_Carmack AGI/low-level optimization takes that makes you think about the problem. 12. @gwern Really high-quality long-form AI research notes and essays. 13. @goodside LLM evaluation, prompting research, and real capabilities testing 14. @drfeifei Computer vision pioneer; human-centered AI and spatial intelligence research 15. @demishassabis Been following his work for 9 years. Demmis is my hope against google usurpating their power with AI. Demmis is Google DeepMind's CEO Let me know who I missed, guys, and save it for the future

Meet Vision Banana 🍌 from @GoogleDeepMind! We provide strong evidence that image generators are generalist vision learners. Traditional computer vision tasks (segmentation, depth estimation, normal prediction) can now be performed at/near SOTA with a single generalist model derived from an image generation model. 🖼️ Explore the results: 📄 See details at:

There have been a lot of crazy many-camera rigs created for the purpose of capturing full spatial video.  I recall a conversation at Meta that was basically “we are going to lean in as hard as possible on classic geometric computer vision before looking at machine learning algorithms”, and I was supportive of that direction. That was many years ago, when ML still felt like unpredictable alchemy, and of course you want to maximize your use of the ground truth! Hardcore engineering effort went into camera calibration, synchronization, and data processing, but  it never really delivered on the vision. No matter how many cameras you have, any complex moving object is going to have occluded areas, and “holes in reality” stand out starkly to a viewer not exactly at one of the camera points. Even when you have good visibility, the ambiguities in multi camera photogrammetry make things less precise than you would like. There were also some experiments to see how good you could make the 3D scene reconstruction from the Quest cameras using offline compute, and the answer was still “not very good”, with quite lumpy surfaces. Lots of 3D reconstructions look amazing scrolling by in the feed on your phone, but not so good blown up to a fully immersive VR rendering and put in contrast to a high quality traditional photo. You really need strong priors to drive the fitting problem and fill in coverage gaps. For architectural scenes, you can get some mileage out of simple planar priors, but modern generative AI is the ultimate prior. Even if the crazy camera rigs fully delivered on the promise, they still wouldn’t have enabled a good content ecosystem. YouTube wouldn’t have succeeded if every creator needed a RED Digital Cinema camera. The (quite good!) stereoscopic 3D photo generation in Quest Instagram is a baby step towards the future. There are paths to stereo video and 6DOF static, then eventually to 6DOF video. Make everything immersive, then allow bespoke tuning of immersive-aware media.

The full talk from which that segment is extracted is here: This is a 2008 Google Tech Talk in which I advocate for 1. Deep learning 2. ConvNets 3. Unsupervised pre-training (already) 4. Energy-based models 5. DL in computer vision and robotics.

A Rubik's cube color extractor. Again, today I'd be tempted to fine-tune some pretrained ConvNet detector on it, but good old computer vision: hough transform + a bunch of heuristics seemed to have worked really well

New paper walkthrough on masked image modeling. Applying the principles of masked language modeling to computer vision. Created by @arig23498 and @RisingSayak

"Fake It Till You Make It: Face analysis in the wild using synthetic data alone" very cool, simulation is on track to become an excellent (if not primary) source of ground truth for many computer vision applications.