SynSeg reports state-of-the-art zero-shot semantic segmentation on four of five benchmarks by combining multi-category contrastive learning with attention-map feature reconstruction.
A Brief Survey and an Application of Semantic Image Segmentation for Autonomous Driving
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abstract
Deep learning is a fast-growing machine learning approach to perceive and understand large amounts of data. In this paper, general information about the deep learning approach which is attracted much attention in the field of machine learning is given in recent years and an application about semantic image segmentation is carried out in order to help autonomous driving of autonomous vehicles. This application is implemented with Fully Convolutional Network (FCN) architectures obtained by modifying the Convolutional Neural Network (CNN) architectures based on deep learning. Experimental studies for the application are utilized 4 different FCN architectures named FCN-AlexNet,FCN-8s, FCN-16s and FCN-32s. For the experimental studies, FCNs are first trained separately and validation accuracies of these trained network models on the used dataset is compared. In addition, image segmentation inferences are visualized to take account of how precisely FCN architectures can segment objects.
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SynSeg: Feature Synergy for Multi-Category Contrastive Learning in End-to-End Open-Vocabulary Semantic Segmentation
SynSeg reports state-of-the-art zero-shot semantic segmentation on four of five benchmarks by combining multi-category contrastive learning with attention-map feature reconstruction.