Weakly Supervised Salient Object Detection by Learning A Classifier-Driven Map Generator
Journal
IEEE Transactions on Image Processing
Journal Volume
28
Journal Issue
11
Pages
5435-5449
Date Issued
2019
Author(s)
Abstract
Top-down saliency detection aims to highlight the regions of a specific object category, and typically relies on pixel-wise annotated training data. In this paper, we address the high cost of collecting such training data by a weakly supervised approach to object saliency detection, where only image-level labels, indicating the presence or absence of a target object in an image, are available. The proposed framework is composed of two collaborative CNN modules, an image-level classifier and a pixel-level map generator. While the former distinguishes images with objects of interest from the rest, the latter is learned to generate saliency maps by which the images masked by the maps can be better predicted by the former. In addition to the top-down guidance from class labels, the map generator is derived by also exploring other cues, including the background prior, superpixel- and object proposal-based evidence. The background prior is introduced to reduce false positives. Evidence from superpixels helps preserve sharp object boundaries. The clue from object proposals improves the integrity of highlighted objects. These different types of cues greatly regularize the training process and reduces the risk of overfitting, which happens frequently when learning CNN models with few training data. Experiments show that our method achieves superior results, even outperforming fully supervised methods. © 1992-2012 IEEE.
Subjects
convolutional neural networks; Top-down object saliency detection; weakly supervised learning
Other Subjects
Neural networks; Object recognition; Superpixels; Supervised learning; Annotated training data; Convolutional neural network; Object categories; Saliency detection; Salient object detection; Supervised methods; Top-down guidances; Weakly supervised learning; Object detection; article; classifier; false positive result; learning; rest
Type
journal article