TinyFormer hybrid detector improves small object detection accuracy

Researchers have introduced TinyFormer, a novel hybrid object detection model designed to improve the identification of small objects. This model combines elements of YOLO and DETR architectures, incorporating Vision Transformer representations and a feature pyramid neck. TinyFormer utilizes a Parallel Bi-fusion Module to maintain high-resolution details and a Spatial Semantic Adapter to compensate for spatial information loss in transformer token embeddings. AI

IMPACT Improves accuracy in detecting small objects, potentially benefiting applications like surveillance and autonomous driving.

RANK_REASON This is a research paper detailing a new model architecture for object detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• MS COCO
• Objects365
• RF-DETR
• TinyFormer
• YOLO

• paper
• model release

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