Domain Generalization alleviates the domain gap between training set and test set, improving performance of deep neural networks on out-of-dataset data. This opens possibility deploying models unlabelled data that were previously pretrained other datasets. In this article, we study ideas RobustNet [Choi et al. CVPR 2021], a recent method for in Urban-Scene Semantic Segmentation. Instead exposin...

Semantic segmentation is a classical problem in computer vision, which important the field of autonomous driving. Although significant progress has been achieved semantic segmentation, its generalization ability to unknown domains still challenging. To effectively solve this problem, method ImDeeplabV3plus with instance selective whitening loss proposed paper. DeeplabV3plus selected as baseline...

Domain adaptation for 3D point cloud has attracted a lot of interest since it can avoid the time-consuming labeling process data to some extent. A recent work named xMUDA leveraged multi-modal domain task semantic segmentation by mimicking predictions between 2D and modalities, outperformed previous single modality methods only using clouds. Based on it, in this paper, we propose novel cross-mo...

Cardiac magnetic resonance imaging (cMRI) is an integral part of diagnosis in many heart related diseases. Recently, deep neural networks have demonstrated successful automatic segmentation, thus alleviating the burden time-consuming manual contouring cardiac structures. Moreover, frameworks such as nnU-Net provide entirely auto- matic model configuration to unseen datasets enabling out-of-the-...

Semantic segmentation is an important task for intelligent vehicles to understand the environment. Current deep learning based methods require large amounts of labeled data training. Manual annotation expensive, while simulators can provide accurate annotations. However, performance semantic model trained with synthetic datasets will significantly degenerate in actual scenes. Unsupervised domai...

This paper presents FogAdapt, a novel approach for domain adaptation of semantic segmentation dense foggy scenes. Although significant research has been directed to reduce the shift in segmentation, scenes with adverse weather conditions remains an open question. Large variations visibility scene due conditions, such as fog, smog, and haze, exacerbate shift, thus making unsupervised scenarios c...

This paper describes the development of a deep learning methodology for semantic utterance classification (SUC) for use in domainspecific dialogue systems. Semantic classifiers need to account for a variety of instances where the utterance for the semantic domain class varies. In order to capture the candidate relationships between the semantic class and the word sequence in an utterance, we ha...

The task of keyword extraction aims at capturing expressions (or entities) that best represent the main topics of a document. Given the rapid adoption of these online semantic annotators and their contribution to the growth of the Semantic Web, one important task is to assess their quality. This article presents an evaluation of the quality and stability of semantic annotators on domain-specifi...