From image-level to pixel-level labeling with Convolutional Networks
We are interested in inferring object segmentation by leveraging only object class information, and by consider-ing only minimal priors on the object segmentation task. This problem could be viewed as a kind of weakly super-vised segmentation task, and naturally fits the Multiple In-stance Learning (MIL) framework: every training image is known to have (or not) at least one pixel corresponding to the image class label, and the segmentation task can be rewritten as inferring the pixels belonging to the class of the object (given one image, and its object class). We pro-pose a Convolutional Neural Network-based model, which is constrained during training to put more weight on pix-els which are important for classifying the image. We show that at test time, the model has learned to discriminate the right pixels well enough, such that it performs very well on an existing segmentation benchmark, by adding only few smoothing priors. Our system is trained using a subset of the Imagenet dataset and the segmentation experiments are performed on the challenging Pascal VOC dataset (with no fine-tuning of the model on Pascal VOC). Our model beats the state of the art results in weakly supervised object seg-mentation task by a large margin. We also compare the per-formance of our model with state of the art fully-supervised segmentation approaches. 1.