220 research outputs found
Spatial Transform Decoupling for Oriented Object Detection
Vision Transformers (ViTs) have achieved remarkable success in computer
vision tasks. However, their potential in rotation-sensitive scenarios has not
been fully explored, and this limitation may be inherently attributed to the
lack of spatial invariance in the data-forwarding process. In this study, we
present a novel approach, termed Spatial Transform Decoupling (STD), providing
a simple-yet-effective solution for oriented object detection with ViTs. Built
upon stacked ViT blocks, STD utilizes separate network branches to predict the
position, size, and angle of bounding boxes, effectively harnessing the spatial
transform potential of ViTs in a divide-and-conquer fashion. Moreover, by
aggregating cascaded activation masks (CAMs) computed upon the regressed
parameters, STD gradually enhances features within regions of interest (RoIs),
which complements the self-attention mechanism. Without bells and whistles, STD
achieves state-of-the-art performance on the benchmark datasets including
DOTA-v1.0 (82.24% mAP) and HRSC2016 (98.55% mAP), which demonstrates the
effectiveness of the proposed method. Source code is available at
https://github.com/yuhongtian17/Spatial-Transform-Decoupling
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