Method and system for weakly supervised salient object detection based on deep learning

Abstract

The invention discloses a method and system for weakly supervised salient object detection based on deep learning. The method includes: using an unsupervised saliency detection method to generate saliency maps of all training images; The category label is used as the noisy supervision information of the first iteration to train a multi-task fully convolutional neural network. After the training process converges, a new category activation map and a salient object prediction map are generated; the conditional random field model is used to adjust the category activation map and the salient object prediction map; use the label update strategy to update the salient label information for the next iteration; carry out the training process for multiple iterations until the stop condition is met; conduct generalization training on a dataset containing images of unknown categories to obtain the final model, the present invention automatically removes noise information during the optimization process, and only uses image-level labeling information to achieve good prediction results, avoiding the tedious and time-consuming pixel-level manual labeling process.

Description

technical field [0001] The present invention relates to the field of computer vision based on deep learning, in particular to a method and system for weakly supervised salient object detection based on deep learning. Background technique [0002] Salient object detection refers to accurately locating the regions in an image that most attract human visual attention. The fact that this technique can be used in many different vision techniques has stimulated a lot of research work in computer vision and cognitive science in recent years. [0003] In recent years, the successful application of convolutional neural networks has brought major breakthroughs to saliency detection technology, such as the research work "Visual saliency based on multiscale deep features" (IEEEConference on Computer Vision and Pattern Recognition (CVPR), June 2015), and the research work of N. Liu et al. in 2016 "Deep hierarchical saliency network for salient object detection" (In Proceedings of the IE...

AI Technical Summary

Problems solved by technology

These methods usually make predictions based on some low-level features, such as color, position, background prior information, etc., which leads to such methods that are always suitable for specific categories of images, but cannot make good predictions for all images. , these low-level feature-based methods have a common disadvantage, that is, most of the detection errors originate from the lack of consideration of spatial correlation and image semantics

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