The hottest Computer Vision Substack posts right now

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Jinay's Substack • 0 implied HN points • 04 Apr 23
🕹 Technology Blogging Software Machine Learning Computer Vision
  1. The author has moved their blog to Substack for its ease of use and wide adoption in the software field.
  2. Older blog posts can still be accessed at the previous blog domain.
  3. Some of the topics covered in the blog include programmatic blogging, backpropagation in machine learning, turning a toy project into a viral challenge, and using computer vision to tell time.

Google AI and Princeton discover this about Deep Learning

Technology Made Simple • 0 implied HN points • 25 Dec 21
🕹 Technology Machine Learning Deep Learning AI Computer Vision NLP
  1. The speed at which a machine learning model 'learns' is influenced by the learning rate, which can make or break the model.
  2. Choosing the correct step size is crucial in machine learning behavior, as highlighted by a study that compared the importance of step size versus direction.
  3. Step size, or the learning rate, seems to be a dominating factor in model learning behavior, showcasing the potential for optimizing performance by combining different optimizer techniques.

Exploring Stable Diffusion

Eddie's startup voyage • 0 implied HN points • 22 Jan 24
🕹 Technology AI Deep Learning Computer Vision
  1. Stable Diffusion is an innovative deep learning model that generates stunning images using latent diffusion techniques in a lower-dimensional space, leading to fast image generation with reduced memory and compute costs.
  2. Diffusion models like Stable Diffusion are important in vision and potentially in language generation and synthetic data creation, showing promise for diverse applications.
  3. Exploring Stable Diffusion and diffusion models can be an intriguing journey in AI, influencing future project choices and sparking curiosity in various research areas.

Reflections on student research projects

Solresol • 0 implied HN points • 27 May 24
🕹 Technology AI Computer Vision Blockchain LLMs Quantum Computing
  1. Many students in the cohort did not train their own computer vision models, instead relying on prompting AI models which proved to be inefficient and not very accurate.
  2. Explainability of results was emphasized in the research projects, with students looking into explaining their models' outcomes.
  3. The compatibility of blockchains with quantum computers is uncertain due to the vulnerability of traditional encryption methods to quantum breaking, leading to ongoing research on solutions.

Newsletter #18: Vision via language

Decoding Coding • 0 implied HN points • 13 Jul 23
🕹 Technology AI Machine Learning Computer Vision Natural Language Data Analysis
  1. LENS uses large language models combined with computer vision to help computers understand images. This means computers can answer questions about visuals using language.
  2. The system has multiple components that analyze images and generate feedback. These include tagging images, describing their attributes, and creating detailed captions.
  3. This approach makes it easier for language models to handle not just images, but potentially videos and other visual inputs in the future, expanding their usefulness.
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Newsletter #15: ViperGPT

Decoding Coding • 0 implied HN points • 15 Jun 23
🕹 Technology Artificial Intelligence Machine Learning Software Development Computer Vision Programming
  1. ViperGPT is a new AI model that can answer questions about images and videos. It combines powerful text and vision models to understand visual inputs better.
  2. The model generates Python code based on user questions, allowing it to be flexible and efficient. It uses all available online Python code for improvement.
  3. ViperGPT's execution engine runs the generated code and provides results based on the visual content. This helps users make sense of raw data in a more meaningful way.

Say Goodbye to Boring Data​​

Sector 6 | The Newsletter of AIM • 0 implied HN points • 16 Feb 23
🕹 Technology Artificial Intelligence Machine Learning Data science Computer Vision
  1. Data scarcity is a big problem for AI and machine learning. New tools like generative AI can help create more data.
  2. Synthetic datasets can be built using techniques like Stable Diffusion. This can make data less boring and more useful for developers.
  3. Generative AI tools can change how we approach data challenges. They offer creative solutions to improve AI development.

Image Data Augmentation using Albumentations

The Beep • 0 implied HN points • 08 May 24
🕹 Technology Machine Learning Computer Vision Data science Software Development Artificial Intelligence
  1. Data augmentation helps improve deep learning models by artificially increasing the size and diversity of training data. This makes models better at understanding new, unseen data.
  2. It's especially useful when there's a limited amount of training data or the data has lots of variations. For example, if images are taken in different lighting or angles, data augmentation can help the model learn to handle those differences.
  3. Albumentations is a fast tool for applying these augmentations in image processing. It allows users to easily create different versions of images to enhance model training.

Context-Aware Meta-Learning For Foundation Models

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Oct 23
🕹 Technology AI Machine Learning Natural Language Processing Computer Vision Data science
  1. Large Language Models can learn quickly from little information during use, without needing extra training. This makes them very flexible in understanding and generating text.
  2. Currently, images don't learn as easily as text when it comes to recognizing new things on the spot. Improving this could allow visual models to learn like language models do.
  3. The new method called Context-Aware Meta-Learning helps visual models learn new concepts right away without extra setup. This can lead to exciting new applications that connect text and images better.

[in case you missed It] Data Science Weekly - Issue 447

Data Science Weekly Newsletter • 0 implied HN points • 19 Jun 22
🕹 Technology AI Data science Machine Learning Natural Language Computer Vision
  1. Natural Language Processing is advancing quickly, with AI starting to mimic human-like conversation. This technology could change how we interact with machines.
  2. DeepMind is using AI for significant medical discoveries, showing real-world applications of machine learning beyond just technology.
  3. There's a debate in the AI community about the limits of scaling language models. Some believe that simply making them bigger may not solve all problems.

[in case you missed it] Data Science Weekly - Issue 391

Data Science Weekly Newsletter • 0 implied HN points • 23 May 21
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Algorithms
  1. Major League Baseball is testing an automated system to call balls and strikes in games. This system aims to make calls accurately and fast so umpires can operate efficiently.
  2. A new tool called Flat makes it easy to manage and version datasets on Git and GitHub. This helps developers work more quickly with data while keeping track of changes.
  3. Twitter improved its image cropping algorithm to better serve all users. After receiving feedback, they are analyzing the model for fairness and accuracy.

[in case you missed it] Data Science Weekly - Issue 381

Data Science Weekly Newsletter • 0 implied HN points • 14 Mar 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Computer Vision
  1. Data sharing in Africa faces challenges due to issues like historical power imbalances and Western-centric policies. It's important to recognize these factors when discussing data access and usage.
  2. Machine learning models can struggle when tested on data that is different from what they were trained on. Research is being done to improve how these models generalize to new situations.
  3. New tools like Dolt combine Git and MySQL to help data scientists collaborate better on datasets. This makes it easier for teams to work together without overwriting each other's changes.

[in case you missed it] Data Science Weekly - Issue 373

Data Science Weekly Newsletter • 0 implied HN points • 17 Jan 21
🕹 Technology Data science Machine Learning Artificial Intelligence Medical Technology Computer Vision
  1. Machine learning is becoming an important tool in developmental biology, helping to analyze large datasets efficiently. It can aid in tasks like image analysis and cell grouping.
  2. There is a growing need for data engineers, with many more job openings in this area compared to data science roles. Training and skills in data engineering are becoming more valuable.
  3. The FDA has released its first action plan for using AI and machine learning in medical software. This shows a commitment to improving healthcare with technology.

[in case you missed it] Data Science Weekly - Issue 360

Data Science Weekly Newsletter • 0 implied HN points • 18 Oct 20
🕹 Technology AI Ethics Machine Learning Data science Computer Vision Human-computer interaction
  1. Making machine learning models run fast on GPUs is important for research and production. It can help speed up improvements and make coding more efficient.
  2. Companies like BMW are creating ethical guidelines for AI use to ensure it benefits people. This is a proactive step to use AI responsibly.
  3. There are various learning resources and tools available for anyone interested in data science. These can help you build a solid foundation and advance your career.

[in case you missed it] Data Science Weekly - Issue 351

Data Science Weekly Newsletter • 0 implied HN points • 16 Aug 20
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Natural Language
  1. The Mona Lisa Effect is a fun digital experience where a portrait's eyes seem to follow you. You can try it by using your webcam.
  2. Maintaining machine learning models in production is challenging, but there are practical ways to manage issues like data contamination and model misbehavior.
  3. AI economics are important to understand, especially for long-tailed data distributions, so that machine learning teams can create better and more profitable AI applications.

[in case you missed it] Data Science Weekly - Issue 307

Data Science Weekly Newsletter • 0 implied HN points • 12 Oct 19
🕹 Technology Data science Artificial Intelligence Machine Learning Computer Vision Software Development
  1. AI needs to learn how to explain its decisions. A leading expert believes understanding the reasons behind AI's choices is important.
  2. Data science is increasingly used in different fields, even fashion. Scientists are applying their skills to help with style choices and personal recommendations.
  3. Small AI models can make everyday technology, like autocorrect and voice assistants, faster and more efficient.

Data Science Weekly - Issue 210

Data Science Weekly Newsletter • 0 implied HN points • 30 Nov 17
🕹 Technology Data science Machine Learning Artificial Intelligence Automation Computer Vision
  1. Computer vision is making big strides, and it's important to keep track of these changes as they can impact society in various ways.
  2. The idea of an 'intelligence explosion' is challenged, suggesting that it's a misunderstanding of how intelligent systems and self-improving technologies function.
  3. Recent studies indicate that many comments about net neutrality may have been faked, highlighting issues with data integrity and trust in public opinions.

Generating a dataset of queries for training and fine-tuning ColPali models on a UFO dataset

machinelearninglibrarian • 0 implied HN points • 23 Sep 24
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Computer Vision
  1. ColPali is a new model that combines text and images to improve how we find documents. It looks at both the words and the visual parts of a page, making it smarter than older text-only methods.
  2. To train ColPali, we need a dataset that pairs document images with questions about what those documents contain. This helps the model learn how to match questions with the right visual information.
  3. Using a special model called Qwen2-VL, we can create specific and relevant queries from images. This can help refine the dataset even more by making sure the questions are useful for retrieving information.

Using Hugging Face AutoTrain to train an image classifier without writing any code.

machinelearninglibrarian • 0 implied HN points • 22 Feb 23
🕹 Technology Machine Learning Artificial Intelligence Data science Computer Vision Software Development
  1. You can train an image classifier with Hugging Face AutoTrain without needing to write any code. This makes it easier for people who aren't programmers to use machine learning.
  2. Image classification is useful for organizing images into categories, like sorting book covers into 'useful' or 'not useful'.
  3. The success of your model often depends more on having good training data than on the model itself. Adjusting and improving your training data can lead to better results.

Training an object detection model using Hugging Face

machinelearninglibrarian • 0 implied HN points • 16 Aug 22
🕹 Technology Machine Learning Computer Vision Artificial Intelligence Data science Programming
  1. Object detection helps identify and locate objects in images. It goes beyond just knowing if something is present; it tells us where and how many of those things are there.
  2. Hugging Face offers tools for training object detection models easily, especially using the Detr architecture. This lets users leverage pre-trained models and datasets for better performance.
  3. Using the datasets library simplifies the data handling process during training. It allows for quick loading and preparation of data, which is very helpful when tweaking and iterating on models.

Using 🤗 datasets for image search

machinelearninglibrarian • 0 implied HN points • 13 Jan 22
🕹 Technology Artificial Intelligence Machine Learning Data science Computer Vision Software Development
  1. You can use the Hugging Face datasets library to create an image search application easily, allowing you to search images effectively.
  2. The library supports different ways to handle images, like reading from file paths or NumPy arrays, which makes it flexible for usage.
  3. It's important to consider potential biases and performance variability when deploying models for image searches, especially with varied datasets.

flyswot

machinelearninglibrarian • 0 implied HN points • 22 Dec 21
🕹 Technology Machine Learning Computer Vision Data Management Image Processing Software Development
  1. The project aims to use computer vision to find and correct mislabeled images in a library's digitized manuscript collection. This will help ensure that images are accurately categorized for future use.
  2. A command line tool called 'flyswot' has been developed to check images for fake labels based on specific filename patterns. This tool helps automate the identification process.
  3. Throughout the project, important lessons were learned about practical machine learning deployment, such as dealing with domain drift and using data version control effectively.

Generative A-Eye #17 - 15th Oct,2024

Martin’s Newsletter • 0 implied HN points • 15 Oct 24
🕹 Technology AI Research Image Synthesis Computer Vision Animation
  1. New tools are being developed to improve how we create and animate 3D characters. These tools help generate human-like movements based on stories or plots.
  2. There are advancements in high-resolution image generation that can produce high-quality images quickly, even on standard laptops. This makes it easier to create detailed visuals without expensive equipment.
  3. Researchers are exploring ways to combine language with video, allowing users to find and interact with events in videos using simple text prompts. This could make video editing and creation more intuitive.

Generative A-Eye #10 - 1st Oct,2024

Martin’s Newsletter • 0 implied HN points • 01 Oct 24
🕹 Technology AI Research Image Synthesis Video Processing Machine Learning Computer Vision
  1. There are some new methods in AI for creating realistic videos of people, which focus on tricky aspects like how loose clothes move.
  2. A new technique in recognizing facial expressions shows better results, improving understanding of how people express emotions.
  3. Some AI projects are working on improving how we replace or animate people in videos, aiming for more realistic and believable results.

Generative A-Eye #4 - 19th Sept,2024

Martin’s Newsletter • 0 implied HN points • 19 Sep 24
🕹 Technology AI Research Image Synthesis Data science Computer Vision Machine Learning
  1. A new method called GaussianHeads can create realistic and dynamic 3D models of human heads using video inputs. This helps capture facial expressions and head movements in real-time.
  2. The research uses a system that combines CGI techniques to enhance the quality of deepfake and human avatar production. It aims to improve how we animate faces based on video footage.
  3. Another interesting paper evaluated AI models by collecting 2 million votes to gauge their effectiveness. This shows the growing need for thorough testing in AI development.

Generative A-Eye #3 - 18th Sept,2024

Martin’s Newsletter • 0 implied HN points • 18 Sep 24
🕹 Technology AI Image Synthesis Computer Vision Deep Learning Generative models
  1. Gaussian Splatting is seen as a strong alternative to traditional deepfake methods, especially for smaller projects like commercials and music videos. Some experts believe it may not be ready for big Hollywood movies yet, but it shows promise.
  2. OmniGen is a new image generation model that simplifies tasks like image editing and can perform many functions without needing extra systems. However, its legality is questionable due to data sources.
  3. A new method for detecting deepfakes uses a phone's vibration to reveal inconsistencies in fake videos, providing a practical solution to identifying deepfakes in real time.

Generative A-Eye #2 - 17th Sept,2024

Martin’s Newsletter • 0 implied HN points • 17 Sep 24
🕹 Technology AI Research Image Synthesis Facial Recognition Computer Vision Data Analysis
  1. The best day for submitting new AI research papers tends to be Tuesday. This timing is likely chosen to catch attention after the weekend.
  2. This year has seen fewer exciting advancements in AI-based human synthesis, with technologies being reused rather than creating entirely new concepts.
  3. New research is focusing on better facial expression recognition and human reconstruction from single images, showing promise in areas like understanding micro-emotions.

Generative A-Eye #1 - 16th Sept,2024

Martin’s Newsletter • 0 implied HN points • 16 Sep 24
🕹 Technology AI Image Synthesis Computer Vision Human-Machine Interaction Data Privacy
  1. InstantDrag offers a new way to edit images by simply dragging, making it easier and faster than using complex commands. It's designed specifically for improving interactivity in image editing tools.
  2. The study on facial expression recognition introduces a method that doesn’t rely on traditional systems, aiming to better understand and represent human emotions. This could open new doors for AI in understanding human feelings.
  3. There's a growing concern about privacy in AI model training, particularly with generative models. Research shows that it's possible to reveal private images used in training, raising important questions about data safety.

Meta introduces HOT3D: A dataset for advancing hand-object interaction research

The PhilaVerse • 0 implied HN points • 03 Jan 25
🕹 Technology Machine Learning Computer Vision Robotics Augmented reality Virtual reality
  1. Meta has created a new dataset called HOT3D to help with research on how humans interact with objects using their hands. It's designed to improve technology in areas like robotics and virtual reality.
  2. The HOT3D dataset is large, with over 833 minutes of video from different angles, showing various tasks with hands and objects. This helps researchers understand interactions better.
  3. The dataset provides detailed information, like 3D poses and eye tracking, which makes it very useful for developing new computer vision and machine learning applications.