State-of-the-art results of semantic segmentation are established by Fully Convolutional neural Networks (FCNs). FCNs rely on cascaded convolutional and pooling layers to gradually enlarge the receptive fields of neurons, resulting in an indirect way of modeling the distant contextual dependence. In this work, we advocate the use of spatially recurrent layers (i.e. ReNet layers) which directly capture global contexts and lead to improved feature representations. We demonstrate the effectiveness of ReNet layers by building a Naive deep ReNet (N-ReNet), which achieves competitive performance on Stanford Background dataset. Furthermore, we integrate ReNet layers with FCNs, and develop a novel Hybrid deep ReNet (H-ReNet). It enjoys a few remarkable properties, including full-image receptive fields, end-to-end training, and efficient network execution. On the PASCAL VOC 2012 benchmark, the H-ReNet improves the results of state-of-the-art approaches Piecewise, CRFasRNN and DeepParsing by 3.6 , 2.3 and 0.2 , respectively, and achieves the highest IoUs for 13 out of the 20 object classes.
. Nonparametric methods have achieved remarkable performance in semantic segmentation @cite_29 @cite_27 @cite_38 @cite_17 @cite_31 . The core idea is retrieving similar patches from a database of fully annotated images, and transferring the labels from the annotated images to the query image. Specifically, the query image is matched against the annotated database using both holistic image representations as well as superpixels. Probabilistic graphical models (e.g. MRF, CRF) are then introduced to model the semantic context and obtain a spatially coherent semantic label map @cite_28 @cite_24 @cite_11 @cite_32 . Nonparametric methods divide the segmentation task into individual steps. Each step requires a careful design, and the entire process is not amenable to joint optimization.
@cite_38 @cite_11 @cite_28 @cite_29 @cite_32 @cite_24 @cite_27 @cite_31 @cite_17
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This paper presents a framework for semantically segmenting a target image without tags by searching for references in an image database, where all the images are unsegmented but annotated with tags. We jointly segment the target image and its references by optimizing both semantic consistencies within individual images and correspondences between the target image and each of its references. In our framework, we first retrieve two types of references with a semantic-driven scheme: i) the compatible references which share similar global appearance with the target image; and ii) the competitive references which have distinct appearance to the target image but similar tags with one of the compatible references. The two types of references have complementary information for assisting the segmentation of the target image. Then we construct a novel graphical representation, in which the vertices are superpixels extracted from the target image and its references. The segmentation problem is posed as labeling all the vertices with the semantic tags obtained from the references. The method is able to label images without the pixel-level annotation and classifier training, and it outperforms the state-of-the-arts approaches on the MSRC-21 database. In this paper, we propose a robust supervised label transfer method for the semantic segmentation of street scenes. Given an input image of street scene, we first find multiple image sets from the training database consisting of images with annotation, each of which can cover all semantic categories in the input image. Then, we establish dense correspondence between the input image and each found image sets with a proposed KNN-MRF matching scheme. It is followed by a matching correspondences classification that tries to reduce the number of semantically incorrect correspondences with trained matching correspondences classification models for different categories. With those matching correspondences classified as semantically correct correspondences, we infer the confidence values of each super pixel belonging to different semantic categories, and integrate them and spatial smoothness constraint in a markov random field to segment the input image. Experiments on three datasets show our method outperforms the traditional learning based methods and the previous nonparametric label transfer method, for the semantic segmentation of street scenes. We present a weakly-supervised approach to semantic segmentation. The goal is to assign pixel-level labels given only partial information, for example, image-level labels. This is an important problem in many application scenarios where it is difficult to get accurate segmentation or not feasible to obtain detailed annotations. The proposed approach starts with an initial coarse segmentation, followed by a spectral clustering approach that groups related image parts into communities. A community-driven graph is then constructed that captures spatial and feature relationships between communities while a label graph captures correlations between image labels. Finally, mapping the image level labels to appropriate communities is formulated as a convex optimization problem. The proposed approach does not require location information for image level labels and can be trained using partially labeled datasets. Compared to the state-of-the-art weakly supervised approaches, we achieve a significant performance improvement of 9 on MSRC-21 dataset and 11 on LabelMe dataset, while being more than 300 times faster. We address the problem of semantic segmentation: classifying each pixel in an image according to the semantic class it belongs to (e.g. dog, road, car). Most existing methods train from fully supervised images, where each pixel is annotated by a class label. To reduce the annotation effort, recently a few weakly supervised approaches emerged. These require only image labels indicating which classes are present. Although their performance reaches a satisfactory level, there is still a substantial gap between the accuracy of fully and weakly supervised methods. We address this gap with a novel active learning method specifically suited for this setting. We model the problem as a pairwise CRF and cast active learning as finding its most informative nodes. These nodes induce the largest expected change in the overall CRF state, after revealing their true label. Our criterion is equivalent to maximizing an upper-bound on accuracy gain. Experiments on two data-sets show that our method achieves 97 percent of the accuracy of the corresponding fully supervised model, while querying less than 17 of the (super-)pixel labels.