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Found 17 papers, 5 papers with code
Language Model Guided Interpretable Video Action Reasoning
Extensive experiments on two complex video action datasets, Charades & CAD-120, validates the improved performance and interpretability of our LaIAR framework.
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach
Automated conversion methods are essential to overcome manual conversion challenges.
A large scale multi-view RGBD visual affordance learning dataset
To date there is no publicly available large dataset for visual affordance understanding and learning.
Scene Graph Generation: A Comprehensive Survey
In this paper, we provide a comprehensive survey of recent achievements in this field brought about by deep learning techniques.
Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks
In practice, the vulnerability of deep learning systems against carefully perturbed images, known as adversarial examples, poses a dire security threat in the physical world applications.
A Systematic Collection of Medical Image Datasets for Deep Learning
Thus, as comprehensive as possible, this paper provides a collection of medical image datasets with their associated challenges for deep learning research.
LCEval: Learned Composite Metric for Caption Evaluation
Automatic evaluation metrics hold a fundamental importance in the development and fine-grained analysis of captioning systems.
WEmbSim: A Simple yet Effective Metric for Image Captioning
The area of automatic image caption evaluation is still undergoing intensive research to address the needs of generating captions which can meet adequacy and fluency requirements.
SubICap: Towards Subword-informed Image Captioning
In this work we address this common limitation of IC systems in dealing with rare words in the corpora.
CommuNety: A Deep Learning System for the Prediction of Cohesive Social Communities
Traditional approaches rely on the analysis of text data related to users to accomplish this task.
Deep Learning Models for Early Detection and Prediction of the spread of Novel Coronavirus (COVID-19)
SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally and has become a pandemic.
Structure-Feature based Graph Self-adaptive Pooling
To solve these problems mentioned above, we propose a novel graph self-adaptive pooling method with the following objectives: (1) to construct a reasonable pooled graph topology, structure and feature information of the graph are considered simultaneously, which provide additional veracity and objectivity in node selection; and (2) to make the pooled nodes contain sufficiently effective graph information, node feature information is aggregated before discarding the unimportant nodes; thus, the selected nodes contain information from neighbor nodes, which can enhance the use of features of the unselected nodes.
Attention in Convolutional LSTM for Gesture Recognition
On this basis, a new variant of LSTM is derived, in which the convolutional structures are only embedded into the input-to-state transition of LSTM.
NNEval: Neural Network based Evaluation Metric for Image Captioning
The automatic evaluation of image descriptions is an intricate task, and it is highly important in the development and fine-grained analysis of captioning systems.
Learning-based Composite Metrics for Improved Caption Evaluation
The evaluation of image caption quality is a challenging task, which requires the assessment of two main aspects in a caption: adequacy and fluency.
Real Time Surveillance for Low Resolution and Limited-Data Scenarios: An Image Set Classification Approach
Class specific gallery subspaces are used to estimate regression models for each image of the test image set.
Efficient Image Set Classification using Linear Regression based Image Reconstruction
We estimate regression models for each test image using the class specific gallery subspaces.
