Multimodal AI

Models that combine images, text, audio, and clinical signals. We cover fusion architectures, missing-modality robustness, and cross-modal alignment, with an emphasis on what actually improves when modalities are combined.

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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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

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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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RideJudge: How an 8B Model Outperforms 32B Baselines at Ride-Hailing Dispute Resolution

RideJudge: How an 8B Model Outperforms 32B Baselines at Ride-Hailing Dispute Resolution

Leave a Comment / Machine Learning, Multimodal AI, Natural Language Processing /

RideJudge: How an 8B Model Outperforms 32B Baselines at Ride-Hailing Dispute Resolution | AI Trend Blend AITrendBlend Machine Learning Computer Vision About LLM Reasoning · Applied AI · arXiv:2603.17328 · Nanjing University & Didi Chuxing (2026) · 19 min read RideJudge: Teaching an 8B Model to Out-Think 32B Rivals on the Hardest Calls in Ride-Hailing

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Think Before You Segment: How TGS-Agent Teaches AI to Reason About Sound Before Picking Up a Brush.

Think Before You Segment: How TGS-Agent Teaches AI to Reason About Sound Before Picking Up a Brush

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Think Before You Segment: How TGS-Agent Teaches AI to Reason About Sound Before Picking Up a Brush | AI Trend Blend Audio-Visual AI · AAAI 2026 · Mohamed Bin Zayed University of AI · 26 min read Think Before You Segment: How TGS-Agent Teaches AI to Reason About Sound Before Picking Up a Brush A

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