Aligning Visual and Lexical Semantics [PDF]
arXiv, 2022 We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics. While visual semantics focus on how humans build concepts when using vision to perceive a target reality, lexical semantics focus on how humans build concepts of the same target reality through the use of language.
arxiv
An ontology to semantically declare and describe functions [PDF]
, 2016 Applications built on top of the Semantic Web are emerging as a novel solution in different areas, such as decision making and route planning. However, to connect results of these solutions -i.e., the semantically annotated data - with real-world ...
De Meester, Ben +3 more
core +1 more source
Visual Re-ranking with Natural Language Understanding for Text Spotting [PDF]
, 2018 Many scene text recognition approaches are based on purely visual information and ignore the semantic relation between scene and text. In this paper, we tackle this problem from natural language processing perspective to fill the gap between language and
Moreno-Noguer, Francesc +2 more
core +3 more sources
A characteristics framework for Semantic Information Systems Standards [PDF]
, 2012 Semantic Information Systems (IS) Standards play a critical role in the development of the networked economy. While their importance is undoubted by all stakeholders—such as businesses, policy makers, researchers, developers—the current state of research
A-W Scheer +51 more
core +3 more sources
DGFNet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images
Remote Sensing, 2021 Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction ability. DCNNs are usually designed as an encoder–decoder architecture
Yongjie Guo +3 more
doaj +1 more source
Unsupervised Domain Adaptation for Semantic Segmentation via Low-level Edge Information Transfer [PDF]
arXiv, 2021 Unsupervised domain adaptation for semantic segmentation aims to make models trained on synthetic data (source domain) adapt to real images (target domain). Previous feature-level adversarial learning methods only consider adapting models on the high-level semantic features.
arxiv
Geometry-Entangled Visual Semantic Transformer for Image Captioning [PDF]
arXiv, 2021 Recent advancements of image captioning have featured Visual-Semantic Fusion or Geometry-Aid attention refinement. However, those fusion-based models, they are still criticized for the lack of geometry information for inter and intra attention refinement.
arxiv
Visual Description Augmented Integration Network for Multimodal Entity and Relation Extraction
Applied Sciences, 2023 Multimodal Named Entity Recognition (MNER) and multimodal Relationship Extraction (MRE) play an important role in processing multimodal data and understanding entity relationships across textual and visual domains.
Min Zuo +5 more
doaj +1 more source
EGO: a personalised multimedia management tool [PDF]
, 2006 The problems of Content-Based Image Retrieval (CBIR) sys- tems can be attributed to the semantic gap between the low-level data representation and the high-level concepts the user associates with images, on the one hand, and the time-varying and often ...
Jose, J.M., Urban, J.
core +1 more source
Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks [PDF]
, 2017 Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images.
Aaron A Berlin (96293) +6 more
core +6 more sources