Learning to Segment Object Candidates
Recent object detection systems rely on two critical steps: (1) a set of object proposals is predicted as efficiently as possible, and (2) this set of candidate proposals is then passed to an object classifier. Such approaches have been shown they can be fast, while achieving the state of the art in detection performance. In this paper, we propose a new way to generate object proposals, introducing an approach based on a discriminative convolutional network. Our model is trained jointly with two objectives: given an image patch, the first part of the system outputs a class-agnostic segmentation mask, while the second part of the system outputs the likelihood of the patch being centered on a full object. At test time, the model is efficiently applied on the whole test image and generates a set of segmentation masks, each of them being assigned with a corresponding object likelihood score. We show that our model yields significant improvements over state-of-theart object proposal algorithms. In particular, compared to previous approaches, our model obtains substantially higher object recall using fewer proposals. We also show that our model is able to generalize to unseen categories it has not seen during training. Unlike all previous approaches for generating object masks, we do not rely on edges, superpixels, or any other form of low-level segmentation.
A neural network inputs an image and generates a set of proposal segmentation masks and an objectness score for each one. It is trained on COCO dataset given the segmentation masks.This is great that the model works well on object categories that it has not seen in training.Why do we want to solve this problem? Reducing the number of candidates can speed up the object detection. I am not sure it can improve the average precision of detection as the total recall for previous approaches like "selective search" is already really high. I think that would be great to apply an off-the-shelf object detection in the output of this method to see the improvement in the speed vs. accuracy. Moreover, this method produces segmentation masks which is great. However, I think it should be compared with the proper segmentation baselines so that we can see the performance.Table 1, Table 2, and Figure 3 show the recall at maximum of 1000 proposals which is not a big number. (Some papers use 2000 candidates for selective search) That would be great to extend it to a larger number say 5000 to see if different approaches converge. For instance, if the recall of this method converges to selective search in 2000 proposals, what is the benefit of this method? I know that this method generates segmentation masks as well, but then, there are quite a few baselines that can be considered to evaluate the segmentation masks. Line 269: that would be great to discuss why downsampling the ground-truth instead of up-sampling the network output degrades the accuracy.The colors on Fig 3 are not easy to read. One can change the pattern of lines (to dots or etc).