Computer Vision

Explore how artificial intelligence teaches machines to interpret and understand the visual world 👁️. Discover the latest breakthroughs in image recognition, 3D generation, and visual data analysis.

RepVIS-GAN: Nighttime Satellite Visible Image Retrieval from Infrared Data.

RepVIS-GAN: Nighttime Satellite Visible Image Retrieval from Infrared Data

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RepVIS-GAN: Nighttime Satellite Visible Image Retrieval from Infrared Data | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Satellite AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 162–174 · 20 min read RepVIS-GAN: Teaching a Satellite to See in the Dark by Reading the Heat It Can Already Feel Every night, […]

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SUP-Net: Deep Learning Fixes Doppler Ultrasound Aliasing by Upsampling the Raw Signal

SUP-Net: Deep Learning Fixes Doppler Ultrasound Aliasing by Upsampling the Raw Signal

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SUP-Net: Deep Learning Fixes Doppler Ultrasound Aliasing by Upsampling the Raw Signal | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Medical AI · IEEE Transactions on Medical Imaging, Vol. 45, No. 1 (Jan 2026) · 18 min read The Aliasing Problem That Breaks Blood Flow Ultrasound — and How SUP-Net Solves It Without

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H2CL: Dual-Geometry Hyperbolic-Euclidean Image-Text Learning for Medical Hierarchical Classification.

H2CL: Dual-Geometry Hyperbolic-Euclidean Image-Text Learning for Medical Hierarchical Classification

Leave a Comment / Machine Learning, Computer Vision, Medical AI /

H2CL: Dual-Geometry Hyperbolic-Euclidean Image-Text Learning for Medical Hierarchical Classification | AI Trend Blend AITrendBlend Machine Learning Computer Vision Medical AI About Medical AI · Medical Image Analysis, Vol. 112 (2026) · 20 min read Why Flat Classifiers Fail Doctors: H²CL Uses Hyperbolic Geometry to Teach AI the Clinical Hierarchy of Disease A UNSW Sydney team

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GTP: The Graph-Transformer That Reads Whole Slide Pathology Images Like a Pathologist

GTP: The Graph-Transformer That Reads Whole Slide Pathology Images Like a Pathologist

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GTP: The Graph-Transformer That Reads Whole Slide Pathology Images Like a Pathologist | AI Trend Blend AITrendBlend Machine Learning Computer Vision Medical AI About Medical AI · IEEE Transactions on Medical Imaging, Vol. 41, Nov. 2022 · 22 min read GTP: The Model That Learned to Read Cancer Slides the Way a Pathologist Actually Does

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Agentic AI for Personalized Knee Braces: How sEMG, Facial Expressions, and LLMs Combine to Configure Rehab Devices

Leave a Comment / Machine Learning, Agent Systems, Computer Vision, Medical AI /

Agentic AI for Personalized Knee Braces: How sEMG, Facial Expressions, and LLMs Combine to Configure Rehab Devices | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Healthcare AI · Advanced Engineering Informatics 74 (2026) 104695 · 22 min read Your Knee Brace Said It Hurts. The AI Didn’t Believe It — Until the Muscle

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PARNet: Dual-Encoder Crack Detection with Dynamic Alignment and Residual Fusion.

PARNet: Dual-Encoder Crack Detection with Dynamic Alignment and Residual Fusion

Leave a Comment / Machine Learning, Computer Vision, Engineering AI /

PARNet: Dual-Encoder Crack Detection with Dynamic Alignment and Residual Fusion | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Computer Vision · Advanced Engineering Informatics 74 (2026) · Shandong University · 20 min read PARNet: The Crack Detection Network That Learned to See Like a Human Inspector — and Then Outperformed Eight of Them

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MEWS: Semantic Segmentation With Almost No Labels — A Few Pixels Per Class Is All You Need.

MEWS: Semantic Segmentation With Almost No Labels — A Few Pixels Per Class Is All You Need

Leave a Comment / Machine Learning, Computer Vision, Image Segmentation /

MEWS: Semantic Segmentation With Almost No Labels — A Few Pixels Per Class Is All You Need | AI Trend Blend AITrendBlend Machine Learning Computer Vision Image Segmentation About Computer Vision · Neurocomputing 680 (2026) 133290 · 18 min read MEWS: The Segmentation Framework That Beats CLIP With Just a Few Pixel Clicks Per Class

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Mask-CDKD: Source-Free Knowledge Distillation from SAM for Satellite Onboard Land Cover Mapping.

Mask-CDKD: Source-Free Knowledge Distillation from SAM for Satellite Onboard Land Cover Mapping

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Mask-CDKD: Source-Free Knowledge Distillation from SAM for Satellite Onboard Land Cover Mapping | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Satellite AI & Remote Sensing · ISPRS J. Photogramm. Remote Sens. 236 (2026) 1–21 · Wuhan University / Emory · 28 min read Teaching a Satellite to See the World Without Labels: How

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Causal Graph Neural Networks for Wildfire Forecasting Across Geographic Shifts.

Causal Graph Neural Networks for Wildfire Forecasting Across Geographic Shifts

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Causal Graph Neural Networks for Wildfire Forecasting Across Geographic Shifts | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Earth Observation & Climate AI · ISPRS J. Photogramm. Remote Sens. 236 (2026) 654–667 · TU Munich / NOA Athens · 27 min read Why Your Wildfire Forecast Fails in Europe When It Was Trained

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Stereo 3D Tracker: Real-Time 3D Point Tracking in Fisheye Stereo Photogrammetry.

Stereo 3D Tracker: Real-Time 3D Point Tracking in Fisheye Stereo Photogrammetry

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Stereo 3D Tracker: Real-Time 3D Point Tracking in Fisheye Stereo Photogrammetry | AI Trend Blend AITrendBlend Machine Learning Computer Vision About 3D Vision & Photogrammetry · ISPRS J. Photogramm. Remote Sens. 236 (2026) 438–455 · K.N. Toosi University of Technology · 25 min read Sub-Millimeter Tracking for $1,000: How the Stereo 3D Tracker Beats Commercial

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