Computer Vision

Computer vision is one of our deepest areas, covering how machines learn to see, segment, and reason about images and video. Articles here range from convolutional and transformer based architectures to dense prediction tasks like detection and segmentation, with regular coverage of medical imaging where reliable vision models carry real clinical weight. The emphasis stays on what makes a method work and where it breaks, backed by the original research.

CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data.

CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data

Leave a Comment / Machine Learning, Computer Vision, Generative & Diffusion Models, Remote Sensing AI /

CRGenNet: Cloud-Free Optical Image Generation Using SAR and Contaminated Optical Data | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Remote Sensing AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 255–272 · 22 min read CRGenNet: How Satellites Can See Through Clouds by Never Assuming the Sky Is Clear Researchers at […]

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GateMamba: Feature Gated Mixer in State Space Model for Point Cloud 3D Object Detection.

GateMamba: Feature Gated Mixer in State Space Model for Point Cloud 3D Object Detection

Leave a Comment / Machine Learning, Computer Vision, Remote Sensing AI, Vision Transformers & Attention /

GateMamba: Feature Gated Mixer in State Space Model for Point Cloud 3D Object Detection | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Autonomous Driving AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 640–653 · 22 min read GateMamba: How Three Gated Mixers Taught a Mamba Network to Stop Ignoring Cyclists

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The Moon's Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction

The Moon’s Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction

Leave a Comment / Machine Learning, Computer Vision, Multimodal AI, Remote Sensing AI, Vision Transformers & Attention /

The Moon’s Many Faces: A Single Unified Transformer for Multimodal Lunar Reconstruction | AI Trend Blend Planetary AI & 3D Reconstruction · ISPRS J. Photogramm. Remote Sens. 236 (2026) 363–379 · TU Dortmund University · 26 min read The Moon’s Many Faces: How One Transformer Learned to Speak All Four Languages of Lunar Science Simultaneously

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CS-EMCF: Compressive Sensing Phase Unwrapping for SAR Interferometry.

CS-EMCF: Compressive Sensing Phase Unwrapping for SAR Interferometry

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CS-EMCF: Compressive Sensing Phase Unwrapping for SAR Interferometry | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Remote Sensing AI · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 120–140 · 22 min read How Compressive Sensing Finally Broke the Phase Unwrapping Bottleneck in SAR Interferometry Researchers at Italy’s CNR-IREA fused decades-old minimum

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GEF: Gaussian Entropy Fields for 3D Surface Reconstruction.

GEF: Gaussian Entropy Fields for 3D Surface Reconstruction

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GEF: Gaussian Entropy Fields for 3D Surface Reconstruction | AI Trend Blend AITrendBlend Machine Learning Computer Vision About 3D Computer Vision · ISPRS Journal of Photogrammetry and Remote Sensing 236 (2026) 273–285 · 24 min read GEF: What If the Secret to Better 3D Reconstruction Was Treating Surface Uncertainty as Entropy? Researchers at Shandong University

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Fusion-Mamba: Hidden State Space Fusion for Cross-Modality Object Detection

Fusion-Mamba: Hidden State Space Fusion for Cross-Modality Object Detection

Leave a Comment / Machine Learning, Computer Vision, Multimodal AI, Vision Transformers & Attention /

Fusion-Mamba: Hidden State Space Fusion for Cross-Modality Object Detection | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Computer Vision · arXiv:2404.09146 · Beihang University · 21 min read Mamba Goes Multimodal: How Fusion-Mamba Built a Hidden State Space to End Modality Disparity Researchers at Beihang University asked what happens when you stop treating

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IRDFusion: Iterative Differential Feedback for Multispectral Object Detection.

IRDFusion: Iterative Differential Feedback for Multispectral Object Detection

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IRDFusion: Iterative Differential Feedback for Multispectral Object Detection | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Computer Vision · arXiv:2509.09085 · Jiangsu University · 20 min read The Feedback Loop That Fixes Multispectral Detection: How IRDFusion Borrowed from Circuit Design to Beat the State of the Art Researchers at Jiangsu University asked a

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SLGNet: Structural Priors and Language-Guided Modulation for Multimodal Object Detection.

SLGNet: Structural Priors and Language-Guided Modulation for Multimodal Object Detection

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

SLGNet: Structural Priors and Language-Guided Modulation for Multimodal Object Detection | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Computer Vision · arXiv:2601.02249 · January 2026 · 22 min read When the Camera Goes Blind: How SLGNet Uses Language and Structure to See in the Dark Researchers at the Chinese Academy of Sciences built

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HMHI-Net: Hierarchical Memory with Heterogeneous Interaction for Video Object Segmentation.

HMHI-Net: Hierarchical Memory with Heterogeneous Interaction for Video Object Segmentation

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HMHI-Net: Hierarchical Memory with Heterogeneous Interaction for Video Object Segmentation | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Computer Vision · ACM Multimedia 2025 · arXiv:2507.22465 · 20 min read Shallow Features Matter: How HMHI-Net Fixes the Fundamental Flaw in Video Object Segmentation Memory Fudan University researchers discovered that every existing memory-based video

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FedLSC: Federated Learning with Layer Similarity Comparison for Skin Cancer.

FedLSC: Federated Learning with Layer Similarity Comparison for Skin Cancer

Leave a Comment / Machine Learning, Computer Vision, Federated Learning & AI Privacy, Medical AI /

FedLSC: Federated Learning with Layer Similarity Comparison for Skin Cancer | AI Trend Blend AITrendBlend Machine Learning Computer Vision Medical AI About Federated Learning · Expert Systems With Applications 306 (2026) 130937 · 22 min read FedLSC: The Smarter Way to Train a Skin Cancer AI Across Hospitals Without Sharing Any Patient Data Researchers at

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