InfraNet lets infrared lead and teaches RGB when to stay quiet in bad weather

新智元 · wechat · 2026-07-26

• Beihang University and collaborators propose InfraNet, an IR-centric framework for robust RGB-IR object detection.
• The motivation is that RGB often degrades in low light, fog, strong backlight, or bad weather, and can actually hurt detection when fused naively with infrared.
• InfraNet treats infrared as the primary branch and uses RGB only as a reliability-controlled training signal.
• Its key module, QualGate, learns when to suppress unreliable RGB guidance and when to amplify infrared features.
• The framework supports both RGB-IR inference and IR-only deployment.
• Reported results include strong performance on LLVIP, FLIR-Aligned, M3FD, and DroneVehicle.
• The main takeaway: robust multimodal perception is not just about adding more modalities, but about learning when to trust each modality.

Original post →

More from Multimodal

• Reddit clip shows an AI-generated aerobics video with stylized workout characters — fox-and-grapes6732 · 2026-07-26
• LingBot-World 2.0 opens a 14B interactive world model with 60 fps real-time generation — WorldofAI · 2026-07-26
• Fable turns a Cezanne-style city builder into a playable AI gesture game — emollick · 2026-07-26
• Opus 5 reportedly delivers remarkable 3D results, despite not winning everywhere — TAbrodi · 2026-07-26
• Yokohama street scene reconstructed with 1,018 photos and 4M 3DGS splats — janusch_patas · 2026-07-26
• AI image tools are getting good at turning composite photos into new compositions — RachelVT42 · 2026-07-26

Multimodal channel →