Deep Attention
37 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Deep Attention
Google Refexp
METU-VIREF Dataset
Latest papers with no code
Deep Attention Recognition for Attack Identification in 5G UAV scenarios: Novel Architecture and End-to-End Evaluation
We compare several performance parameters in our proposed Deep Network.
Deep Attention-Based Alignment Network for Melody Generation from Incomplete Lyrics
We propose a deep attention-based alignment network, which aims to automatically predict lyrics and melody with given incomplete lyrics as input in a way similar to the music creation of humans.
ProstAttention-Net: A deep attention model for prostate cancer segmentation by aggressiveness in MRI scans
In this work, we propose a novel end-to-end multi-class network that jointly segments the prostate gland and cancer lesions with GS group grading.
Revisiting Attention Weights as Explanations from an Information Theoretic Perspective
Attention mechanisms have recently demonstrated impressive performance on a range of NLP tasks, and attention scores are often used as a proxy for model explainability.
AttTrack: Online Deep Attention Transfer for Multi-object Tracking
Multi-object tracking (MOT) is a vital component of intelligent video analytics applications such as surveillance and autonomous driving.
Fuzzy Attention Neural Network to Tackle Discontinuity in Airway Segmentation
especially for cohorts with different lung diseases.
A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations
In addition, compared to the published models for genetic alterations, AMIML provided a significant improvement for predicting a wide range of genes (e. g., KMT2C, TP53, and SETD2 for KIRC; ERBB2, BRCA1, and BRCA2 for BRCA; JAK1, POLE, and MTOR for UCEC) as well as produced outstanding predictive models for other clinically relevant gene mutations, which have not been reported in the current literature.
Visual Attention Methods in Deep Learning: An In-Depth Survey
Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data.
WebFormer: The Web-page Transformer for Structure Information Extraction
Structure information extraction refers to the task of extracting structured text fields from web pages, such as extracting a product offer from a shopping page including product title, description, brand and price.
Deep Attention-Based Supernovae Classification of Multi-Band Light-Curves
We offer three main contributions: 1) Based on temporal modulation and attention mechanisms, we propose a Deep attention model (TimeModAttn) to classify multi-band light-curves of different SN types, avoiding photometric or hand-crafted feature computations, missing-value assumptions, and explicit imputation/interpolation methods.
