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AVR: Attention based Salient Visual Relationship Detection
Visual relationship detection aims to locate objects in images and recognize the relationships between objects. Traditional methods treat all observed relationships in an image... -
Continuum Attention for Neural Operators
The dataset is not explicitly described in the paper, but it is mentioned that the authors used it to train and test their neural operator architectures. -
Food-101: Mining Discriminative Components with Random Forests
Food-101: mining discriminative components with random forests. -
Automated Flower Classification over a Large Number of Classes
Automated flower classification over a large number of classes. -
3D Object Representations for Fine-Grained Categorization
3D object representations for fine-grained categorization. -
Fully Convolutional Attention Networks for Fine-Grained Recognition
Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations such as poses. A key to address this problem is to... -
XNMT: The eXtensible Neural Machine Translation Toolkit
XNMT is a neural machine translation toolkit that focuses on modular code design, making it easy to swap in and out different parts of the model. -
Attention Based Simple Primitives for Open World Compositional Zero Shot Lear...
Compositional Zero-Shot Learning (CZSL) aims to predict unknown compositions made up of attribute and object pairs. -
Medical Transformer: Gated axial-attention for medical image segmentation
Medical Transformer: Gated axial-attention for medical image segmentation. -
COVID-19 Detection from Pulmonary CT Scans using a Novel EfficientNet with At...
COVID-19 Detection from Pulmonary CT Scans using a Novel EfficientNet with Attention Mechanism -
SPANet: Salient Positions-based Attention Network for Image Classification
The proposed SPANet selectively gathers contextual information from the salient positions in the low level and stops the error drift between network layers. -
A Weakly-Supervised Depth Estimation Network Using Attention Mechanism
A weakly-supervised depth estimation network using attention mechanism for cases with wrong labels. -
MR, Subj, SST-1, SST-2, MPQA
The dataset used in this paper for text classification task. -
Graph Neural Networks Including SparSe inTerpretability (GISST)
Graph Neural Networks (GNNs) are versatile, powerful machine learning methods that enable graph structure and feature representation learning, and have applications across many... -
PAtt-Lite: Lightweight Patch and Attention
Facial Expression Recognition (FER) is a machine learning problem that deals with recognizing human facial expressions. -
Vision Big Bird
Vision Big Bird: Random Sparsification for Full Attention -
Deep Attention Recurrent Q-Network
The Deep Attention Recurrent Q-Network (DARQN) algorithm was tested on several popular Atari 2600 games: Breakout, Seaquest, Space Invaders, Tutankham, and Gopher. -
Spatially Attentive Output Layer for Image Classification
The proposed SAOL improves the performances of representative architectures for various tasks, with almost the same computational cost. Moreover, additional self-supervision... -
Focusing Attention: Towards Accurate Text Recognition in Natural Images
Focusing attention: Towards accurate text recognition in natural images. -
SCAN: Sliding Convolutional Attention Network for Scene Text Recognition
Scene text recognition has drawn great attentions in the community of computer vision and artificial intelligence due to its challenges and wide applications. The proposed...
