Satya Mallick @LearnOpenCV
CEO, https://t.co/CzUdJlxzJM. Course Director, https://t.co/O2Tz9vUOQ8 Entrepreneur. Ph.D. ( Computer Vision & Machine Learning ). Author: https://t.co/olraDEG5Ue YouTube.com/c/LearnOpenCV San Diego, CA Joined June 2008-
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Generic multi-token prediction is fast but breaks object detection — it doesn't know where one box ends and the next begins. LocateAnything's fix: make the prediction block = the box itself. The block isn't arbitrary. The block IS the geometry. 3 modes: 🐢 Slow — token-by-token, stable ⚡ Fast — parallel blocks, throughput 🔀 Hybrid — fast by default, falls back on hard cases Don't force geometry into a text generation mode. Part 2 of "Why Can't AI Point to the Exact Pixels Where Objects Are?" vist.ly/56vtn #LocateAnything #ComputerVision #VLM
I want to offer some unsolicited advice to computer vision researchers jumping into robotics. Don't focus too much on VLMs, VLAs etc. That's fine, but the real action is at the sensorimotor level. Most of the open problems in robotics are in manipulation, which is about hand-object interaction, and contacts and forces are central. Proprioception and tactile sensing are as important as vision. Don't get seduced by cherry-picked demos. You can't do robotics without doing robotics.
Most VLMs predict bounding boxes one token at a time — X1, Y1, X2, Y2. But a box isn't text. It's geometry. NVIDIA's LocateAnything predicts the entire box as one atomic unit. Parallel Box Decoding > next-token prediction for spatial outputs. (Part 1 🧵) Breakdown 👇 learnopencv.com #ComputerVision #AI
📣 AMD is now an OpenCV 5 Launch Partner & Gold Sponsor! We're teaming up to bring first-class CPU + GPU acceleration to OpenCV 5, speeding up Vision AI pre- & post-processing across AMD Ryzen™, RDNA™ & ROCm™. Read more opencv.org/opencv-and-amd… #OpenCV #AMD #ComputerVision #AI
Instead of training a new model, what if you could just tell your AI what to look for? That's YOLOE-26 with text prompts. Hand it a list — Person, Helmet, Safety vest — get instance segmentation masks, not just boxes. Full tutorial: vist.ly/56iqy
An AI can tell you there's a cat in the image. Pointing to the exact pixels is the hard part. The reason it's slow: most VLMs spell out a bounding box one coordinate token at a time — some even split "1024" into single digits. But a box's corners are connected. Decode them independently and errors compound. That's the wall the next gen of clicking, navigating AI agents has to break. Full breakdown 👇 vist.ly/56dq3
Weak AI vs Strong AI, in one line: Weak AI recognizes the cat in the photo. Strong AI debates climate change with you. One is already transforming industries. The other could revolutionize everything we know. Full breakdown 👇 vist.ly/569tx #AI #AGI #MachineLearning
Euler went blind in 1771 at 64. He published 1 paper per week in 1775. After dying in 1783 he published 228 more papers. A third of all papers on math, mathematical physics & engineering mechanics in the latter part of the 18th century were his. If he was still being cited, his h-index would be around 850.
YOLOE-26 turns object detection into three ways of saying "find this": → Text prompt (name it) → Visual prompt (show it) → Prompt-free (let the model decide) Closed-set rigidity → open-vocabulary conversation. Tutorial + benchmarks: vist.ly/565gr
Object detection is shifting from "models that recognize fixed categories" to "models that understand concepts described in language." YOLOE delivers open-vocabulary detection at full YOLO speed — text module fused into the head, zero runtime overhead. Full tutorial + code: vist.ly/55v2b
Those are orthogonal concepts. - World models trained on highly diverse data become foundation models: their encoders can be used for a wide variety of downstream tasks. - "World" refers to two things: (1) predicting the evolution of a complex system or environment, (2) predicting the evolution of a system under control and its effect on the environment (action-conditioned world model) which is a necessary component of planning.
RF-DETR is now available in @huggingface transformers state of the art in both detection and segmentation, outperforming YOLO architectures - checkpoints: huggingface.co/Roboflow/models - demo: huggingface.co/spaces/hugging… - docs: huggingface.co/collections/me…
This robot's only job is to pretend it's your eyeball 👁️🤖 At Display Week 2026, Dr. Satya Mallick visits Gamma Scientific — the 6-axis robot AR/VR brands use to QA every headset before launch. 18+ tests in one rig: contrast, parallax, MTF, color gamut, eye box. The invisible layer behind every Vision Pro. #ARVR #DisplayWeek2026 #Metrology #GammaScientific #Robotics #VisionPro
A $99 hologram. With an AI agent living inside it. Dr. Satya Mallick meets Shawn Frayne (CEO, Looking Glass Factory) at Display Week 2026 for a hands-on with the Looking Glass Go + their new life-size Hololuminescent Display — SID 2026 Display of the Year. The future of display isn't a headset. 🧵 #LookingGlass #Hologram #AI #DisplayWeek2026 #LightField #SpatialComputing #Hololuminescent
MoE Training, Part 2 — in one tweet: You start with random weights. By chance, one expert is slightly better at legal questions. Router notices, sends more its way. It gets better. Snowballs. Same compounding loop that turns a slightly-talented 7-year-old into an IMO medalist. — Dr. Satya Mallick, CEO @ OpenCV vist.ly/55552
MoE Training, Part 1 — in one tweet: You do NOT assign "this expert handles medicine, this one handles law." You start with 9 random experts + a router. The router learns to pick 2–3 per question. Specialization emerges from data, not design. That's how Mixtral and DeepSeek scale. — Dr. Satya Mallick, CEO @ OpenCV vist.ly/54y4c
Almost the entire editorial board of the Journal of Approximation Theory just resigned after a dispute with the publisher. Almost. You can’t make this stuff up.
Detection: finds "a car." Grounding: finds the red car in the crowd of cars. Detection = fixed classes + bounding box (YOLO, RF-DETR). Grounding = free-form language → localization. The word "grounding" comes from cognitive scientist Stevan Harnad (1990) — mapping abstract symbols to physical reality. CV borrowed it in the 2010s. Full breakdown: vist.ly/54tih
François Chollet @fchollet
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1.8M Followers 159 Following Values are most enduring thing leaders create. Engineering pioneer, proud grandfather, philanthropist, Christian, farm boy at heart.Trends for United States
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