1 research outputs found
Unbiasing Semantic Segmentation For Robot Perception using Synthetic Data Feature Transfer
Robot perception systems need to perform reliable image segmentation in
real-time on noisy, raw perception data. State-of-the-art segmentation
approaches use large CNN models and carefully constructed datasets; however,
these models focus on accuracy at the cost of real-time inference. Furthermore,
the standard semantic segmentation datasets are not large enough for training
CNNs without augmentation and are not representative of noisy, uncurated robot
perception data. We propose improving the performance of real-time segmentation
frameworks on robot perception data by transferring features learned from
synthetic segmentation data. We show that pretraining real-time segmentation
architectures with synthetic segmentation data instead of ImageNet improves
fine-tuning performance by reducing the bias learned in pretraining and closing
the \textit{transfer gap} as a result. Our experiments show that our real-time
robot perception models pretrained on synthetic data outperform those
pretrained on ImageNet for every scale of fine-tuning data examined. Moreover,
the degree to which synthetic pretraining outperforms ImageNet pretraining
increases as the availability of robot data decreases, making our approach
attractive for robotics domains where dataset collection is hard and/or
expensive