Salient Object Detection via Multiple Instance Joint Re-Learning

In recent years deep neural networks have been widely applied to visual saliency detection tasks with remarkable detection performance improvements. As for the salient object detection in single image, the automatically computed convolutional features frequently demonstrate high discriminative power to distinguish salient foregrounds from its non-salient surroundings in most cases. Yet, the obstinate feature conflicts still persist, which naturally gives rise to the learning ambiguity, arriving at massive failure detections. To solve such problem, we propose to jointly re-learn common consistency of inter-image saliency and then use it to boost the detection performance. Its core rationale is to utilize the easy-to-detect cases to re-boost much harder ones. Compared with the conventional methods, which focus on their problem domain within the single image scope, our method attempts to utilize those beyond-scope information to facilitate the current salient object detection. To validate our new approach, we have conducted a comprehensive quantitative comparisons between our approach and 13 state-of-the-art methods over 5 publicly available benchmarks, and all the results suggest the advantage of our approach in terms of accuracy, reliability, and versatility.