SAL-Net: Self-Supervised Attribute Learning for Object Recognition and Segmentation

Figure 3

The proposed multiobject recognition and segmentation system pipeline. In the training phase, the input image is fed into the attribute generation module to obtain positive samples and the region proposal module to obtain negative samples. Then, the positive and negative samples are utilized to train the parameters of SAL-Net. In the testing phase, the input image is fed into the region proposal module to obtain testing samples, which are sent to the SAL-Net for prediction. Finally, the recognition and segmentation results are sent to the postprocessing module for filtering.