Faster R-CNN

CD-FKD: Cross-Domain Feature Knowledge Distillation for Robust Object Detection

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CD-FKD: Cross-Domain Feature Knowledge Distillation for Robust Object Detection | AI Trend Blend Autonomous Driving · Object Detection · arXiv:2603.16439 | LG Electronics · Naver · GIST (2026) · 20 min read CD-FKD: Teaching Your Object Detector to See in the Dark, Rain, and Fog — With Only Sunny-Day Training Data Researchers from LG Electronics, […]

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Scientific visualization of YOLO-FCE model outperforming older AI detection systems in identifying Australian wildlife species.

7 Reasons Why YOLO-FCE Outshines Traditional Models (And One Critical Flaw)

3 Comments / Machine Learning /

Australia is home to over 600 mammal species, 800 bird species, and countless reptiles and amphibians — many found nowhere else on Earth. Yet, as biodiversity declines at an alarming rate, accurate, fast, and scalable species identification has become a critical challenge for conservationists. Enter YOLO-FCE, a groundbreaking AI model that’s redefining how we detect

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DAHI framework for small object detection

7 Revolutionary Breakthroughs in Small Object Detection: The DAHI Framework

3 Comments / Machine Learning /

Detecting tiny vehicles in drone footage. Spotting distant pedestrians in smart city surveillance. Identifying miniature components on a factory floor. These are the critical challenges facing modern computer vision—where small object detection (SOD) isn’t just a technical hurdle, but a make-or-break factor for safety, automation, and intelligence. Despite decades of progress, most deep learning models

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