Advanced Computer Vision Techniques

AI that counts sheep. Not the kind that helps you sleep. This footage shows AI models counting and tracking sheep with accuracy that would take humans hours to achieve manually. Agriculture is being transformed by computer vision that can detect, count, and monitor livestock at scale. Farmers managing thousands of animals can now get precise counts instantly instead of manual tallies that are always approximate. But the applications extend far beyond counting. The same technology detects health issues by identifying animals moving differently. → Tracks growth rates.  → Monitors feeding patterns.  → Identifies animals that need veterinary attention before visible symptoms appear. This is precision agriculture enabled by AI that can process visual information faster and more consistently than human observation. The technology applies to crops as well. → Detecting disease in plants. Identifying optimal harvest timing.  → Monitoring soil conditions.  → Tracking equipment across vast properties. Agriculture has always been about managing biological systems at scale. AI gives farmers tools to observe and respond to those systems with precision that was never possible before. The revolution is giving farmers capabilities to manage complexity that overwhelmed manual observation. What other industries have observation problems that computer vision could solve at scale?

Computer vision is about to get its second wave. Most people associate it with image classification or AI-generated video. That’s yesterday’s framing. The real shift is happening around world models, systems that don’t just label pixels, but learn structured representations of how the physical world works. Labs led by people like Yann LeCun and Fei-Fei Li are pushing in this direction: → perception tied to prediction → vision tied to action → models that understand space, not just images This matters because: * Robotics stops being brittle * Physical AI becomes trainable, not hard-coded * Simulation → real-world transfer actually works We may be approaching a “ChatGPT moment” for vision, not because of better images, but because models can reason about the visual world. That’s a very different capability.

#MIT's new "Radial Attention" makes Generative Video 4.4x cheaper to train and 3.7x faster to run. Here's why: The problem with current AI video? It's BRUTALLY expensive. Every frame must "pay attention" to every other frame. With thousands of frames, costs explode exponentially. Training one model? $100K+ Running it? Painfully slow. Massachusetts Institute of Technology, NVIDIA, Princeton, UC Berkeley, Stanford, and First Intelligence just changed the game. Their breakthrough insight: Video attention works like physics. - Sound gets quieter with distance - Light dims as it travels - Heat dissipates over space Turns out, AI video tokens follow the same rules. Why waste compute power on distant, irrelevant connections? Enter Radial Attention: Instead of checking EVERY connection: • Nearby frames → full attention • Distant frames → sparse attention • Computation scales logarithmically, not quadratically Technical result: O(n log n) vs O(n²) Translation: MASSIVE efficiency gains Real-world results on production models: 📊 HunyuanVideo (Tencent): • 2.78x training speedup • 2.35x inference speedup 📊 Mochi 1: • 1.78x training speedup • 1.63x inference speedup Quality? Maintained or IMPROVED. What this unlocks: 4x longer videos, same resources 4.4x cheaper training costs 3.7x faster generation Works with existing models (no retraining!) And, MIT open-sourced everything: https://lnkd.in/gETYw8eT The bigger picture: The internet is transforming. BEFORE: A place to store videos from the real world NOW: A machine that generates synthetic content on demand Think about it: • TikTok filled with AI-generated content • YouTube creators using AI for entire videos • Streaming services producing personalized shows • Educational content generated for each student This changes everything. Remember when only big tech could afford image AI? 2020: GPT-3 → Only OpenAI 2022: Stable Diffusion → Everyone 2024: Midjourney everywhere Video AI is next. Radial Attention probably just accelerated the timeline. The future isn't coming. It's here. And it's more accessible than ever. Want to ride this wave? → Follow me for weekly AI breakthroughs → Share if this opened your eyes → Try the code: https://lnkd.in/gETYw8eT What will YOU create when video AI costs 4x less? #AI #VideoGeneration #MachineLearning #TechInnovation #FutureOfContent

Happy Friday! This week in #learnwithmz, let’s talk about how AI “sees” the world through Vision Language Models (VLMs). We often treat AI as text-only, but modern models like Gemini, DeepSeek-VL and GPT-4o, etc. blend vision and language, allowing them to describe, reason about, and even “imagine” what they see. An excellent article by Frederik Vom Lehn mapped out how information flows inside a VLM, from raw pixels all the way to text predictions. What’s going on inside a VLM? - Early layers detect colors and simple patterns. - Middle layers respond to shapes, edges, and structures. - Later layers align visual regions with linguistic concepts: like “dog,” “street,” or “sky.” - Vision tokens have large L2 norms, which makes them less sensitive to spatial order (a “bag-of-visual-features” effect). - The attention mechanism favors text tokens, suggesting that language often dominates reasoning. - You can even use softmax probabilities to segment images or detect hallucinations in multimodal outputs. Why it Matters? Understanding how VLMs allocate attention helps explain why they sometimes hallucinate objects or struggle with spatial reasoning. PMs & Builders If you’re working with multimodal AI, think copilots, chat with images, or agentic vision, invest time in visual explainability. It’s understanding how AI perceives. Read the full visualization breakdown here: https://lnkd.in/gc2pZnt2 #AI #VisionLanguageModels #LLMs #ProductManagement #learnwithmz #DeepLearning #MultimodalAI

I built a set of command-line tools that let you generate, edit, and analyze images through Unix pipes - beautifully simple on Mac and Linux, and probably works on Windows too. These tools work perfectly with Google's brand new Gemini 2.5 Flash Image (nicely codenamed nano-banana). And at ~$0.039 per image through OpenRouter, you can actually afford to experiment and benchmark these models. Here's the simple case - generate a new image: 𝗴𝗿𝗮𝗳𝘁 -𝗽 "𝗔𝗻 𝗛𝗗 𝗽𝗵𝗼𝘁𝗼 𝗼𝗳 𝗮 𝗰𝘆𝗯𝗲𝗿𝗽𝘂𝗻𝗸 𝘀𝘁𝗿𝗲𝗲𝘁 𝗺𝗮𝗿𝗸𝗲𝘁 𝗮𝘁 𝗻𝗶𝗴𝗵𝘁" To make it more interesting, we can grab an image from the web and modify it: 𝗰𝘂𝗿𝗹 𝗵𝘁𝘁𝗽𝘀://𝗰𝗱𝗻.𝗻𝗮𝗶𝗱𝗮.𝗮𝗶/𝗺𝗶𝘀𝗰/𝘀𝗴𝟮𝟬𝟰𝟵.𝗽𝗻𝗴 | 𝗴𝗿𝗮𝗳𝘁 -𝗽 "𝗮𝗱𝗱 𝘀𝗼𝗺𝗲 𝗳𝗹𝘆𝗶𝗻𝗴 𝗱𝗿𝗼𝗻𝗲𝘀" That's a futuristic Singapore skyline, and now it has drones. Pipe it through glimpse to verify what changed, chain multiple edits, build entire workflows. Want to test if an AI model really understands photography styles? Run this: 𝗳𝗼𝗿 𝗱𝗲𝗰𝗮𝗱𝗲 𝗶𝗻 𝟭𝟵𝟱𝟬 𝟭𝟵𝟲𝟬 𝟭𝟵𝟳𝟬 𝟭𝟵𝟴𝟬 𝟭𝟵𝟵𝟬 𝟮𝟬𝟬𝟬; 𝗱𝗼  𝗴𝗿𝗮𝗳𝘁 -𝗽 "𝘀𝘁𝗿𝗲𝗲𝘁 𝘀𝗰𝗲𝗻𝗲, 𝗮𝘂𝘁𝗵𝗲𝗻𝘁𝗶𝗰 ${𝗱𝗲𝗰𝗮𝗱𝗲}𝘀 𝗽𝗵𝗼𝘁𝗼𝗴𝗿𝗮𝗽𝗵" -𝗼 - |   𝗴𝗹𝗶𝗺𝗽𝘀𝗲 -𝗺 <𝘀𝗼𝗺𝗲_𝗼𝘁𝗵𝗲𝗿_𝗺𝗼𝗱𝗲𝗹> -𝗽 "𝘄𝗵𝗮𝘁 𝗱𝗲𝗰𝗮𝗱𝗲 𝘄𝗮𝘀 𝘁𝗵𝗶𝘀 𝗽𝗵𝗼𝘁𝗼 𝘁𝗮𝗸𝗲𝗻?" 𝗱𝗼𝗻𝗲 You now have data on whether the model actually knows what makes a 1970s photo look like the 1970s. Run it 100 times with different temperatures, build a confusion matrix, find the edge cases where models hallucinate or ignore instructions. Configure glimpse to use high-end vision models like Gemini 2.5 Pro, GPT-5 or Claude 4 Sonnet to evaluate the outputs from smaller, cheaper, faster generation models - proper benchmarking without breaking the bank. For researchers evaluating image models, this beats clicking through web interfaces or writing complex evaluation scripts. Everything is scriptable, reproducible, and measurable. Export to CSV, track model performance over time, integrate into your CI/CD pipeline to catch regressions. The Unix philosophy wins big here: small tools that do one thing well, composed into powerful pipelines = rapid research & benchmarking. Code is on GitHub at u1i/graft if you want to try it yourself.

Exciting advancements in Text-to-Video Retrieval (T2VR) from The Johns Hopkins University and Johns Hopkins Applied Physics Laboratory! Introducing Video-ColBERT → a breakthrough retrieval method enhancing similarity between language queries and videos. Here's why it's a game-changer: 1. MeanMaxSim (MMS)   -> Efficiently handles variable query lengths by using mean instead of sum for token-wise similarity. 2. Dual-Level Tokenwise Interaction   -> Independently analyzes static frames and dynamic temporal features for deeper video insights. 3. Query and Visual Expansion   -> Adds tokens to both queries and videos, capturing richer, more relevant content. 4. Dual Sigmoid Loss Training   -> Strengthens spatial and temporal data individually, boosting retrieval accuracy and robustness. This isn't just theory. Video-ColBERT achieved state-of-the-art results on major benchmarks like MSR-VTT, MSVD, VATEX, DiDeMo, and ActivityNet. Simply put: Better tech = more accurate video retrieval. Massive leap forward from Johns Hopkins and DEVCOM Army Research Laboratory!

Introducing SAM 3D: Powerful 3D Reconstruction for Physical World Images and it’s not your typical 3D reconstruction tool. It does what previous models couldn’t: - Reconstruct real-world objects and scenes from a single image. - Handle occlusion, indirect views, and cluttered backgrounds. - Estimate human pose and shape with surprising accuracy. Why does this matter? Because for the first time, we’re seeing 3D perception at the scale, quality, and accessibility of today’s 2D models. The architecture behind SAM 3D borrows from LLMs: pre-training on synthetic data, followed by post-training on real-world images using a human-in-the-loop ranking engine. The result is a feedback loop that continuously improves both the data and the model. The implications stretch far beyond creative media. Robotics. E-commerce. Sports medicine. Interactive avatars. You name it. And the best part? It's fast. Real-time fast. Meta’s already using SAM 3D to power a “View in Room” feature in Facebook Marketplace; turning static listings into immersive experiences. The gap between virtual and physical is shrinking. SAM 3D is a serious leap forward. Learn more: - 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗮𝗻𝗻𝗼𝘂𝗻𝗰𝗲𝗺𝗲𝗻𝘁 𝗯𝗹𝗼𝗴: https://lnkd.in/g8w6dvAB - 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gd_HQE9c - 𝗥𝗲𝗮𝗱 𝘁𝗵𝗲 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗣𝗮𝗽𝗲𝗿: https://lnkd.in/gp3p9jaf - 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗢𝗯𝗷𝗲𝗰𝘁𝘀: https://lnkd.in/gfwmcKGK - 𝗗𝗼𝘄𝗻𝗹𝗼𝗮𝗱 𝗦𝗔𝗠 𝟯𝗗 𝗕𝗼𝗱𝘆: https://lnkd.in/g9CPHEXJ - 𝗘𝘅𝗽𝗹𝗼𝗿𝗲 𝘁𝗵𝗲 𝗣𝗹𝗮𝘆𝗴𝗿𝗼𝘂𝗻𝗱: https://lnkd.in/guSDTnU3

Meta just introduced #SAM-3D, a model that can turn a single photo into a complete 3D scene - geometry, texture, pose, and even hidden structure. It works on real, cluttered images where previous models failed. Why it’s a breakthrough: SAM-3D doesn’t just fill in visible pixels. It reconstructs the full 3D shape and places objects correctly in the scene. This is the closest step yet toward a general 3D foundation model. How Meta achieved it: - A hybrid “human + model-in-the-loop” pipeline - Nearly 1M real images - 3.14M meshes - LLM-style pretrain → mid-train → post-train → DPO alignment Performance gains: - 5× human preference wins on object reconstructions - 6× win on full scenes - Best-in-class Chamfer distance (0.0400) - Geometry inference reduced from 25 steps to 4 Why it matters: - This raises the bar for robotics, AR/VR, gaming, advertising, and any workflow that needs fast, accurate 3D. With SAM-3D, Meta is positioning itself at the front of spatial AI. #AI #3DReconstruction #ComputerVision #SpatialAI #GenerativeAI #DeepLearning #AR #VR #Robotics #MetaAI

With the CVPR conference happening this week, I’m excited to share my recent interview with Fatih Porikli from Qualcomm AI Research. We discussed several of their 16 accepted main track and workshop papers. The papers span both generative and conventional computer vision, with a focus on emerging techniques for achieving improved efficiency for mobile and edge devices. Top reasons to check out this episode: • Dig into several cutting-edge methods for enhancing performance and efficiency of text-to-image diffusion models like StableDiffusion • Learn about a new dataset and LLM for offering proactive feedback and coaching in scenarios like fitness personal training • Explore how training models visually reason over mathematical plots promises to enhance their ability to focus on important details • Catch up on many more of the latest ideas in visual generative AI and traditional computer vision #ComputerVision #AI #GenerativeAI https://lnkd.in/eSqPxWnh

This week's defining shift for me is that creating 3D data is getting much simpler. New tools are turning everyday inputs like smartphone video, single photos, and text prompts into usable 3D environments and assets. This lowers the barrier to building the scenes, objects, and spaces that robotics, simulation, and immersive content rely on. It also shifts 3D creation from a specialized skill to something all teams can generate quickly and at the scale modern spatial systems require. This week’s news surfaced signals like these: 🤖 Parallax Worlds raised $4.9 million to turn standard video into digital twins for robotics testing. The platform turns basic walkthrough videos into interactive 3D spaces that teams can use to run their robot software and see how it performs before sending anything into the field. 🪑 Meta introduced SAM 3D to reconstruct objects and people from single images, producing full-textured meshes even when subjects are partly hidden or shot from difficult angles. The models were trained using real-world data and a staged process to improve accuracy. 🌏 Meta unveiled WorldGen, a research tool that generates full 3D worlds from text prompts. It produces complete, navigable spaces that can be used in Unity or Unreal and shows how AI can create environments without manual modeling. Why this matters: Faster 3D pipelines expand who can build, test, and refine spatial ideas. They turn 3D creation from a bottleneck into a regular part of development, which opens the door to more experimentation and better decisions earlier in the process. #robotics #digitaltwins #simulation #VR #AR #virtualreality #spatialcomputing #physicalAI #AI #3D