Möbius Transform for Mitigating Perspective Distortions in Representation Learning
arxiv(2024)
摘要
Perspective distortion (PD) causes unprecedented changes in shape, size,
orientation, angles, and other spatial relationships of visual concepts in
images. Precisely estimating camera intrinsic and extrinsic parameters is a
challenging task that prevents synthesizing perspective distortion.
Non-availability of dedicated training data poses a critical barrier to
developing robust computer vision methods. Additionally, distortion correction
methods make other computer vision tasks a multi-step approach and lack
performance. In this work, we propose mitigating perspective distortion (MPD)
by employing a fine-grained parameter control on a specific family of Möbius
transform to model real-world distortion without estimating camera intrinsic
and extrinsic parameters and without the need for actual distorted data. Also,
we present a dedicated perspectively distorted benchmark dataset, ImageNet-PD,
to benchmark the robustness of deep learning models against this new dataset.
The proposed method outperforms on existing benchmarks, ImageNet-E and
ImageNet-X. Additionally, it significantly improves performance on ImageNet-PD
while consistently performing on standard data distribution. Further, our
method shows improved performance on three PD-affected real-world applications:
crowd counting, fisheye image recognition, and person re-identification. We
will release source code, dataset, and models for foster further research.
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