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

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

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

在 arXiv cs.AI 阅读 →

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

• 论文
• 模型发布

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