EdgeCrafter: Compact ViTs for Edge Dense Prediction via Task-Specialized Distillation
This work addresses the challenge of achieving both accuracy and efficiency with compact Vision Transformers (ViTs) on resource-constrained edge devices for dense prediction tasks. To this end, the authors propose EdgeCrafter, a framework that integrates task-specialized knowledge distillation, a lightweight ViT backbone, and an edge-friendly encoder-decoder architecture, significantly enhancing the performance of small-scale ViTs using only task-specific annotations. Experimental results demonstrate that ECDet-S achieves 51.7 AP on COCO object detection with fewer than 10M parameters; ECInsSeg matches the instance segmentation performance of RF-DETR with reduced model size; and ECPose-X attains 74.8 AP in pose estimation, substantially outperforming YOLO26Pose-X—which relies on large-scale pretraining—thereby validating the superior accuracy-parameter efficiency trade-off of the proposed approach.