Möbius Transform for Mitigating Perspective Distortions in Representation Learning

• URL: http://arxiv.org/abs/2405.02296v2
• Date: Mon, 15 Jul 2024 14:16:52 GMT
• Title: Möbius Transform for Mitigating Perspective Distortions in Representation Learning
• Authors: Prakash Chandra Chhipa, Meenakshi Subhash Chippa, Kanjar De, Rajkumar Saini, Marcus Liwicki, Mubarak Shah,
• Abstract summary: Perspective distortion (PD) causes unprecedented changes in shape, size, orientation, angles, and other spatial relationships in images. We propose mitigating perspective distortion (MPD) by employing a fine-grained parameter control on a specific family of M"obius transform. We present a dedicated perspectively distorted benchmark dataset, ImageNet-PD, to benchmark the robustness of deep learning models against this new dataset.
• Score: 43.86985901138407
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 M\"obius 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 existing benchmarks, ImageNet-E and ImageNet-X. Additionally, it significantly improves performance on ImageNet-PD while consistently performing on standard data distribution. Notably, our method shows improved performance on three PD-affected real-world applications crowd counting, fisheye image recognition, and person re-identification and one PD-affected challenging CV task: object detection. The source code, dataset, and models are available on the project webpage at https://prakashchhipa.github.io/projects/mpd.

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