Mean Box Pooling: A Rich Image Representation and Output Embedding for the Visual Madlibs Task
Table 2: Accuracies computed for different number of Edge Box proposals on the easy and hard tasks of the Visual Madlibs dataset. The NMS threshold 0.75 and mean-pooling is used for all the experiments. Results in %.
['[BOLD] Easy Task Number of proposals', '[BOLD] Easy Task 10', '[BOLD] Easy Task 25', '[BOLD] Easy Task 50', '[BOLD] Easy Task 100']
The maximal number of object proposals is the second factor of Edge Boxes that we study in this work. A larger number of proposals tend to cover a larger fraction of the input image. Moreover, the higher number together with the higher NMS threshold can assign proposals to both an object, and its parts, effectively forming a multi-scale and multi-parts object representation. The experiments suggest using a larger numbers of proposals, however the gain diminishes with the larger numbers.