Results 31 to 40 of about 504,050 (265)
Convolutional Self-Attention Networks [PDF]
Proceedings of the 2019 Conference of the North, 2019 Self-attention network (SAN) has recently attracted increasing interest due to its fully parallelized computation and flexibility in modeling dependencies. It can be further enhanced with multi-headed attention mechanism by allowing the model to jointly attend to information from different representation subspaces at different positions (Vaswani et al.,
Baosong Yang +4 more
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Multi-Image Super Resolution of Remotely Sensed Images Using Residual Attention Deep Neural Networks
Remote Sensing, 2020 Convolutional Neural Networks (CNNs) consistently proved state-of-the-art results in image Super-resolution (SR), representing an exceptional opportunity for the remote sensing field to extract further information and knowledge from captured data ...
Francesco Salvetti +3 more
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AMBCR: Low‐light image enhancement via attention guided multi‐branch construction and Retinex theory
IET Image Processing, 2021 Due to different lighting environments and equipment limitations, low‐light images have high noise, low contrast and unobvious colours. The main purpose of low‐light image enhancement is to preserve the details and suppress noise as much as possible ...
Miao Li +4 more
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CoRR, 2016 In this work, we propose Attentive Pooling (AP), a two-way attention mechanism for discriminative model training. In the context of pair-wise ranking or classification with neural networks, AP enables the pooling layer to be aware of the current input pair, in a way that information from the two input items can directly influence the computation of ...
Cícero Nogueira dos Santos +3 more
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An attention-modulated associative network [PDF]
Learning & Behavior, 2010 We present an elemental model of associative learning that describes interactions between stimulus elements as a process of competitive normalization. Building on the assumptions laid out in Harris (2006), stimuli are represented as an array of elements that compete for attention according to the strength of their input.
Harris, Justin A, Livesey, Evan J
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Hyperbolic Graph Attention Network [PDF]
IEEE Transactions on Big Data, 2021 Graph neural network (GNN) has shown superior performance in dealing with graphs, which has attracted considerable research attention recently. However, most of the existing GNN models are primarily designed for graphs in Euclidean spaces. Recent research has proven that the graph data exhibits non-Euclidean latent anatomy.
Yiding Zhang +4 more
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Anatomy of attentional networks [PDF]
The Anatomical Record Part B: The New Anatomist, 2004 AbstractAttention is a central theme in psychological science. As with other biological systems, attention has a distinct anatomy that carries out basic psychological functions. Disparate attentional networks correlate with discrete neural circuitry and can be influenced by specific brain injuries, states, and drugs.
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Functional Specialization in the Attention Network [PDF]
Annual Review of Psychology, 2020 Spatial attention is comprised of neural mechanisms that boost sensory processing at a behaviorally relevant location while filtering out competing information. The present review examines functional specialization in the network of brain regions that directs such preferential processing.
Ian C, Fiebelkorn, Sabine, Kastner
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Special attention network [PDF]
Physica A: Statistical Mechanics and its Applications, 2004 5 pages, no ...
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Attention in Attention Network for Image Super-Resolution
CoRR, 2021 Convolutional neural networks have allowed remarkable advances in single image super-resolution (SISR) over the last decade. Among recent advances in SISR, attention mechanisms are crucial for high-performance SR models. However, the attention mechanism remains unclear on why and how it works in SISR.
Haoyu Chen 0003, Jinjin Gu, Zhi Zhang
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