Weakly supervised object detection with correlation and part suppression
In weakly supervised object detection, conventional methods treat object location in each image as a latent variable and use non convex optimization to solve the latent variable. However, as the optimization objective is image level instead of sample level, the learning procedure tends to choose object parts as false positive samples. Furthermore, when multiple classes of objects appear in the same images, the models could invite class correlations and lose discriminative capability. In this paper, we propose a simple but effective suppression strategy that mines hard negative samples in the learning procedure to ease the above problems. We propose using a spatial voting strategy to help finding negative samples to suppress the impact of object parts. We also use regions from class correlated images as negative samples to suppress the impact of class correlations. Experiments show that our approach significantly improves the baseline by 6% and achieves state of the art performance.
2016 IEEE International Conference on Image Processing (ICIP)