Results 61 to 70 of about 33,786 (306)
Transformer-based progressive residual network for single image dehazing
Frontiers in Neurorobotics, 2022 IntroductionThe seriously degraded fogging image affects the further visual tasks. How to obtain a fog-free image is not only challenging, but also important in computer vision.
Zhe Yang +4 more
doaj +1 more source
Arthritis Care &Research, EarlyView.Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Semantic-Aware Local-Global Vision Transformer
, 2022 Vision Transformers have achieved remarkable progresses, among which Swin Transformer has demonstrated the tremendous potential of Transformer for vision tasks.
Zhang, Jiatong +4 more
core +1 more source
What Do Large Language Models Know About Materials?
Advanced Engineering Materials, EarlyView.If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer +2 more
wiley +1 more source
Transformer With Linear-Window Attention for Feature Matching
IEEE Access, 2023 A transformer can capture long-term dependencies through an attention mechanism, and hence, can be applied to various vision tasks. However, its secondary computational complexity is a major obstacle in vision tasks that require accurate predictions.
Zhiwei Shen, Bin Kong, Xiaoyu Dong
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Vision Language Transformers: A Survey
CoRR, 2023 Vision language tasks, such as answering questions about or generating captions that describe an image, are difficult tasks for computers to perform. A relatively recent body of research has adapted the pretrained transformer architecture introduced in \citet{vaswani2017attention} to vision language modeling.
Clayton Fields, Casey Kennington
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Advanced Engineering Materials, EarlyView.This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi +4 more
wiley +1 more source
Frontiers in Artificial IntelligenceEfficient waste management is crucial for urban environments to maintain cleanliness, reduce environmental impact, and optimize resource allocation. Traditional waste collection systems often rely on scheduled pickups or manual inspections, leading to ...
Parakram Singh Tanwer +4 more
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IEEE Access, 2023 Traditionally, the success of the Transformer has been attributed to its token mixer, particularly the self-attention mechanism. However, recent studies suggest that replacing such attention-based token mixer with alternative techniques can yield ...
Dongwook Yang, Seung-Woo Seo
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Accepted to ECCV2024. Project website: https://jiawei-yang.github.io/DenoisingViT/
Jiawei Yang 0002 +8 more
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