The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost. Detailed per-pixel annotations enable training accurate models but are very time-consuming to obtain, image-level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image-level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and objectness potential yields an improvement of 12.9 mIOU over image-level supervision. Further, we demonstrate that models trained with point-level supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget.
Types of Supervision for Semantic Segmentation To reduce the up-front annotation time for semantic segmentation, recent works have focused on training models in a weakly- or semi-supervised setting. Many forms of supervision have been explored, such as eye tracks @cite_37 , free-form squiggles @cite_18 @cite_9 , noisy web tags @cite_26 , size constraints on objects @cite_23 or heterogeneous annotations @cite_15 . Common settings are image-level labels @cite_13 @cite_11 @cite_14 and bounding boxes @cite_13 @cite_1 . @cite_28 @cite_22 @cite_33 use co-segmentation methods trained from image-level labels to automatically infer the segmentations. @cite_23 @cite_11 @cite_14 train CNNs supervised only with image-level labels by extending the Multiple-Instance Learning (MIL) framework for semantic segmentation. @cite_13 @cite_1 use an EM procedure, which alternates between estimating pixel labels from bounding box annotations and optimizing the parameters of a CNN.
@cite_37 @cite_26 @cite_18 @cite_14 @cite_22 @cite_33 @cite_28 @cite_9 @cite_1 @cite_23 @cite_15 @cite_13 @cite_11
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We address the task of learning a semantic segmentation from weakly supervised data. Our aim is to devise a system that predicts an object label for each pixel by making use of only image level labels during training – the information whether a certain object is present or not in the image. Such coarse tagging of images is faster and easier to obtain as opposed to the tedious task of pixelwise labeling required in state of the art systems. We cast this task naturally as a multiple instance learning (MIL) problem. We use Semantic Texton Forest (STF) as the basic framework and extend it for the MIL setting. We make use of multitask learning (MTL) to regularize our solution. Here, an external task of geometric context estimation is used to improve on the task of semantic segmentation. We report experimental results on the MSRC21 and the very challenging VOC2007 datasets. On MSRC21 dataset we are able, by using 276 weakly labeled images, to achieve the performance of a supervised STF trained on pixelwise labeled training set of 56 images, which is a significant reduction in supervision needed. The semantic image segmentation task presents a trade-off between test time accuracy and training-time annotation cost. Detailed per-pixel annotations enable training accurate models but are very time-consuming to obtain, image-level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image-level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and objectness potential yields an improvement of 12.9 mIOU over image-level supervision. Further, we demonstrate that models trained with point-level supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget. The semantic image segmentation task presents a trade-off between test time accuracy and training time annotation cost. Detailed per-pixel annotations enable training accurate models but are very time-consuming to obtain; image-level class labels are an order of magnitude cheaper but result in less accurate models. We take a natural step from image-level annotation towards stronger supervision: we ask annotators to point to an object if one exists. We incorporate this point supervision along with a novel objectness potential in the training loss function of a CNN model. Experimental results on the PASCAL VOC 2012 benchmark reveal that the combined effect of point-level supervision and objectness potential yields an improvement of (12.9 , ) mIOU over image-level supervision. Further, we demonstrate that models trained with point-level supervision are more accurate than models trained with image-level, squiggle-level or full supervision given a fixed annotation budget. This paper addresses the problem of weakly supervised semantic image segmentation. Our goal is to label every pixel in a new image, given only image-level object labels associated with training images. Our problem statement differs from common semantic segmentation, where pixel-wise annotations are typically assumed available in training. We specify a novel deep architecture which fuses three distinct computation processes toward semantic segmentation &#x2013; namely, (i) the bottom-up computation of neural activations in a CNN for the image-level prediction of object classes, (ii) the top-down estimation of conditional likelihoods of the CNNs activations given the predicted objects, resulting in probabilistic attention maps per object class, and (iii) the lateral attention-message passing from neighboring neurons at the same CNN layer. The fusion of (i)-(iii) is realized via a conditional random field as recurrent network aimed at generating a smooth and boundary-preserving segmentation. Unlike existing work, we formulate a unified end-to-end learning of all components of our deep architecture. Evaluation on the benchmark PASCAL VOC 2012 dataset demonstrates that we outperform reasonable weakly supervised baselines and state-of-the-art approaches.