Image semantic segmentation network continuous learning method, system, device and storage medium

Abstract

The invention discloses a continuous learning method, system, equipment and storage medium for image semantic segmentation network. On the one hand, the method of extracting old knowledge representations and aligning them through nonlinear transformation in feature space effectively maintains the invariance of old knowledge and improves the Ability to learn new knowledge. On the other hand, the topology of the new category is optimized in the embedding space, and the invariance of the topology of the old category is maintained to reduce forgetting and prevent confusion between classes. This makes it unnecessary to provide labels of old categories in the continuous learning of semantic segmentation, reducing the cost of labeling. In general, as a universal semantic segmentation continuous learning method, the present invention has no restrictions on application scenarios, and has strong generalization ability and practical value.

Description

technical field [0001] The present invention relates to the technical field of image semantic segmentation, in particular to a continuous learning method, system, device and storage medium for image semantic segmentation network. Background technique [0002] In recent years, deep neural networks have achieved great success in semantic segmentation tasks. However, the traditional semantic segmentation network training method needs to obtain all the training data at one time, and it is difficult to update after the training is completed. In practical applications, the network is often required to gradually learn and update the learned knowledge from the data stream, thereby effectively reducing the cost of data storage and training. But having deep neural networks learn directly on new data can lead to severe forgetting of what has been learned. The continuous learning technology imposes additional constraints on the learning process to achieve the purpose of learning new k...

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Problems solved by technology

However, it still cannot solve the unique challenges in the aforementioned continuous learning task of semantic segmentation, so it cannot be directly applied in image semantic segmentation

The Chinese patent application with publication number CN103366163A "Face Detection System and Method Based on Incremental Learning", the Chinese patent application with publication number CN106897705A "A Method for Distribution of Ocean Observation Big Data Based on Incremental Learning", and the publication number is The Chinese patent application of CN103593680A "A Dynamic Gesture Recognition Method Based on Hidden Markov Model Incremental Learning" is a method dedicated to a specific field, and it cannot be proved that it has generalization and universality

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