SemanticAware Scene Recognition Abstract   Scene recognition is currently one of the topchallenging research fields in computer vision. This may be due to the ambiguity between classes: images of several scene classes may share similar objects, which causes confusion among them. The problem is aggravated when images of a particular scene class are notably different. Convolutional Neural Networks (CNNs) have significantly boosted performance in scene recognition, albeit it is still far below from other recognition tasks (e.g., object or image recognition). In this paper, we describe a novel approach for scene recognition based on an endtoend multimodal CNN that combines image and context information by means of an attention module. Context information, in the shape of a semantic segmentation, is used to gate features extracted from the RGB image by leveraging on information encoded in the semantic representation: the set of scene objects and stuff, and their relative locations. This gating process reinforces the learning of indicative scene content and enhances scene disambiguation by refocusing the receptive fields of the CNN towards them. Experimental results on three publicly available datasets show that the proposed approach outperforms every other stateoftheart method while significantly reducing the number of network parameters. All the code and data used along this paper is available at:  https:github.comvpulabSemanticAwareSceneRecognition

Preserving details in semanticsaware context for scene parsing Great success of scene parsing (also known as, semantic segmentation) has been achieved with the pipeline of fully convolutional networks (FCNs). Nevertheless, there are a lot of segmentation failures caused by large similarities between local appearances. To alleviate the problem, most of existing methods attempt to improve the global view of FCNs by introducing different contextual modules. Though the reconstructed high resolution output of these methods is of rich semantics, it cannot faithfully recover the fine image details owing to lack of desired precise lowlevel information. To overcome the problem, we propose to improve the spatial decoding process through embedding possibly lost lowlevel information in a principled way. To this end, we make the following three contributions. First, we propose a semantics conformity module to make lowlevel features variations agnostic. Second, we introduce semantics into the conformed low level features through guidance from semantically aware features. Finally, we institute the availability of various possible contextual features at feature fusion to enrich context information. The proposed approach demonstrates competitive performance on challenging PASCAL VOC 2012, Cityscapes, and ADE20K benchmarks in comparison to the stateoftheart methods.