Deep Attention

33 papers with code • 0 benchmarks • 2 datasets

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Most implemented papers

PREDATOR: Registration of 3D Point Clouds with Low Overlap

ShengyuH/OverlapPredator CVPR 2021

We introduce PREDATOR, a model for pairwise point-cloud registration with deep attention to the overlap region.

Deep Attention Recurrent Q-Network

5vision/DARQN 5 Dec 2015

A deep learning approach to reinforcement learning led to a general learner able to train on visual input to play a variety of arcade games at the human and superhuman levels.

Your Local GAN: Designing Two Dimensional Local Attention Mechanisms for Generative Models

giannisdaras/ylg CVPR 2020

We introduce a new local sparse attention layer that preserves two-dimensional geometry and locality.

Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates

gvalvano/multiscale-adversarial-attention-gates 2 Jul 2020

We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks.

Processing Megapixel Images with Deep Attention-Sampling Models

idiap/attention-sampling 3 May 2019

We show that sampling from the attention distribution results in an unbiased estimator of the full model with minimal variance, and we derive an unbiased estimator of the gradient that we use to train our model end-to-end with a normal SGD procedure.

Deep multi-stations weather forecasting: explainable recurrent convolutional neural networks

IsmailAlaouiAbdellaoui/weather-forecasting-explanable-recurrent-convolutional-NN 23 Sep 2020

Deep learning applied to weather forecasting has started gaining popularity because of the progress achieved by data-driven models.

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing

salesforce/TabularSemanticParsing Findings of the Association for Computational Linguistics 2020

We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing.

Deep attention-based classification network for robust depth prediction

keerthi165/depthEstimation 11 Jul 2018

However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?

RA-UNet: A hybrid deep attention-aware network to extract liver and tumor in CT scans

RanSuLab/RAUNet-tumor-segmentation 4 Nov 2018

Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes.

Multi-scale self-guided attention for medical image segmentation

sinAshish/Multi-Scale-Attention arXiv preprint 2019

In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms.