125 research outputs found
Oriented Response Networks
Deep Convolution Neural Networks (DCNNs) are capable of learning
unprecedentedly effective image representations. However, their ability in
handling significant local and global image rotations remains limited. In this
paper, we propose Active Rotating Filters (ARFs) that actively rotate during
convolution and produce feature maps with location and orientation explicitly
encoded. An ARF acts as a virtual filter bank containing the filter itself and
its multiple unmaterialised rotated versions. During back-propagation, an ARF
is collectively updated using errors from all its rotated versions. DCNNs using
ARFs, referred to as Oriented Response Networks (ORNs), can produce
within-class rotation-invariant deep features while maintaining inter-class
discrimination for classification tasks. The oriented response produced by ORNs
can also be used for image and object orientation estimation tasks. Over
multiple state-of-the-art DCNN architectures, such as VGG, ResNet, and STN, we
consistently observe that replacing regular filters with the proposed ARFs
leads to significant reduction in the number of network parameters and
improvement in classification performance. We report the best results on
several commonly used benchmarks.Comment: Accepted in CVPR 2017. Source code available at http://yzhou.work/OR
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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