Robust Object Detection with Multi-input Multi-output Faster R-CNN

• URL: http://arxiv.org/abs/2111.13065v1
• Date: Thu, 25 Nov 2021 12:59:34 GMT
• Title: Robust Object Detection with Multi-input Multi-output Faster R-CNN
• Authors: Sebastian Cygert, Andrzej Czyzewski
• Abstract summary: This work applies the multi-input multi-output architecture (MIMO) to the task of object detection using the general-purpose Faster R-CNN model. MIMO allows building strong feature representation and obtains very competitive accuracy when using just two input/output pairs. It also adds just 0.5% additional model parameters and increases the inference time by 15.9% when compared to the standard Faster R-CNN.
• Score: 2.9823962001574182
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have seen impressive progress in visual recognition on many benchmarks, however, generalization to the real-world in out-of-distribution setting remains a significant challenge. A state-of-the-art method for robust visual recognition is model ensembling. however, recently it was shown that similarly competitive results could be achieved with a much smaller cost, by using multi-input multi-output architecture (MIMO). In this work, a generalization of the MIMO approach is applied to the task of object detection using the general-purpose Faster R-CNN model. It was shown that using the MIMO framework allows building strong feature representation and obtains very competitive accuracy when using just two input/output pairs. Furthermore, it adds just 0.5\% additional model parameters and increases the inference time by 15.9\% when compared to the standard Faster R-CNN. It also works comparably to, or outperforms the Deep Ensemble approach in terms of model accuracy, robustness to out-of-distribution setting, and uncertainty calibration when the same number of predictions is used. This work opens up avenues for applying the MIMO approach in other high-level tasks such as semantic segmentation and depth estimation.

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