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arXiv Computer Vision By Longfei Liu, Yongjie Hou, Yang Li, Qirui Wang, Youyang Sha, Yongjun Yu, Yinzhi Wang, Peizhe Ru, Xuanlong Yu, Xi Shen

EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation

Read the original on arXiv Computer Vision →

EdgeCrafter presents a compact Vision Transformer framework tailored for edge dense prediction tasks such as object detection, instance segmentation, and human pose estimation. By employing task-specialized distillation and an edge-friendly encoder-decoder design, the ECDet backbone achieves 51.7 box AP with under 10 M parameters, while its extensions reach 48.4 mask AP and 74.8 keypoint AP using only COCO annotations. Pretraining on Objects365 further boosts performance, demonstrating that lightweight ViTs can compete with CNN-based models on resource-constrained devices.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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