ANDA: A Novel Data Augmentation Technique Applied to Salient Object Detection
This addresses the problem of data scarcity and overfitting in Salient Object Detection for computer vision applications, but it is incremental as it builds on existing augmentation methods.
The paper tackles the limited generalization of standard data augmentation in Salient Object Detection by proposing ANDA, a technique that creates new images by combining objects with new backgrounds using linear combination and inpainting, resulting in improvements of up to 14.1% in F-measure and reductions of up to 2.6% in Mean Absolute Error depending on the network.
In this paper, we propose a novel data augmentation technique (ANDA) applied to the Salient Object Detection (SOD) context. Standard data augmentation techniques proposed in the literature, such as image cropping, rotation, flipping, and resizing, only generate variations of the existing examples, providing a limited generalization. Our method has the novelty of creating new images, by combining an object with a new background while retaining part of its salience in this new context; To do so, the ANDA technique relies on the linear combination between labeled salient objects and new backgrounds, generated by removing the original salient object in a process known as image inpainting. Our proposed technique allows for more precise control of the object's position and size while preserving background information. Aiming to evaluate our proposed method, we trained multiple deep neural networks and compared the effect that our technique has in each one. We also compared our method with other data augmentation techniques. Our findings show that depending on the network improvement can be up to 14.1% in the F-measure and decay of up to 2.6% in the Mean Absolute Error.