Results 91 to 100 of about 328,450 (323)

DBSANet: A Dual-Branch Semantic Aggregation Network Integrating CNNs and Transformers for Landslide Detection in Remote Sensing Images

Remote Sensing
Deep learning-based semantic segmentation algorithms have proven effective in landslide detection. For the past decade, convolutional neural networks (CNNs) have been the prevailing approach for semantic segmentation.
Yankui Li, Wu Zhu, Jing Wu, Ruixuan Zhang, Xueyong Xu, Ye Zhou   +5 more
doaj   +1 more source

Enhanced Text Matching Based on Semantic Transformation

IEEE Access, 2020
Text matching is the core of natural language processing (NLP) system. It's considered as a touchstone of the NLP, and it aims to find whether text pairs are equal in semantics. However, the semantic gap in text matching is still an open problem to solve.
Shutao Zhang, Haibo Tan, Liangfeng Chen, Bo Lv   +3 more
doaj   +1 more source

Bridging the gap between social tagging and semantic annotation: E.D. the Entity Describer [PDF]

, 2007
Semantic annotation enables the development of efficient computational methods for analyzing and interacting with information, thus maximizing its value.
Benjamin M. Good, Edward A. Kawas, Mark D. Wilkinson   +2 more
core   +1 more source

Why my photos look sideways or upside down? Detecting Canonical Orientation of Images using Convolutional Neural Networks

, 2017
Image orientation detection requires high-level scene understanding. Humans use object recognition and contextual scene information to correctly orient images.
Deshpande, Pranav P., Khandelwal, Gaurav, Swami, Kunal, Vijayvargiya, Ajay   +3 more
core   +1 more source

Handling Ontology Gaps in Semantic Parsing

Proceedings of the 13th Joint Conference on Lexical and Computational Semantics (*SEM 2024)
The majority of Neural Semantic Parsing (NSP) models are developed with the assumption that there are no concepts outside the ones such models can represent with their target symbols (closed-world assumption). This assumption leads to generate hallucinated outputs rather than admitting their lack of knowledge.
Andrea Bacciu, Marco Damonte, Marco Basaldella, Emilio Monti   +3 more
openaire   +3 more sources

Single‐Nucleus Multi‐Omics Reveals Hypoxia‐Driven Angiogenic Programs and Their Epigenetic Control in Sinonasal Squamous Cell Carcinoma

Advanced Science, EarlyView.
Single‐nucleus multi‐omics profiling of sinonasal squamous cell carcinoma unveils a hypoxia‐driven angiogenic axis. A specific hypoxic tumor subpopulation orchestrates endothelial tip cell differentiation via epigenetically regulated ADM and VEGFA secretion.
Chaelin You, Jaewoo Park, Jung Yeon Jang, Junho Noh, Jaehyun Lee, Geunho Kwon, Myeong Sang Yu, Yoo‐Sam Chung, Seung‐Jun Lee, Keunsoo Kang, Jihwan Park, Ji Heui Kim, Kyuho Kang   +12 more
wiley   +1 more source

SemWeB Semantic Web Browser – Improving Browsing Experience with Semantic and Personalized Information and Hyperlinks

, 2009
Imagine a Web browser that can understand the context of a Web page and recommends related semantic hyperlinks in any Web domain. In addition, imagine this browser also understands your browsing needs and personalizes information for you.
De Roure, David, Hall, Wendy, Sah, Melike   +2 more
core  

Semantic Segmentation of Foggy Scenes Based on Progressive Domain Gap Decoupling [PDF]

, 2023
Ziquan Wang, Yongsheng Zhang, Ying Yu, Zhenchao Zhang, Zhipeng Jiang, Binbin Cheng   +5 more
openalex   +1 more source

Foundation Model‐Enabled Multimodal Deep Learning for Prognostic Prediction in Colorectal Cancer with Incomplete Modalities: A Multi‐Institutional Retrospective Study

Advanced Science, EarlyView.
FLARE, a multimodal AI framework, combines pathology slides, radiology scans, and clinical reports to predict colorectal cancer outcomes, even when some tests are missing. Evaluated retrospectively in 1679 patients from four medical centers, it consistently achieved the best prognostic accuracy and clearly separated high‐ and low‐risk groups.
Linhao Qu, Chengsheng Zhang, Yingyong Hou, Feng Tang, Weiqi Sheng, Dan Huang, Zhijian Song   +6 more
wiley   +1 more source

An Empirical Study and Analysis of Generalized Zero-Shot Learning for Object Recognition in the Wild

, 2016
Zero-shot learning (ZSL) methods have been studied in the unrealistic setting where test data are assumed to come from unseen classes only. In this paper, we advocate studying the problem of generalized zero-shot learning (GZSL) where the test data's ...
Changpinyo, Soravit, Chao, Wei-Lun, Gong, Boqing, Sha, Fei   +3 more
core   +1 more source