Search Results for author:
Found 9 papers, 3 papers with code
The two-way knowledge interaction interface between humans and neural networks
Through experimental validation, based on this interaction interface, NN can provide humans with easily understandable explanations of the reasoning process.
Spatial Parsing and Dynamic Temporal Pooling networks for Human-Object Interaction detection
However, there is still room for improvement in video HOI detection performance.
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.
Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition
With the effective spatio-temporal relationship modeling, it is possible not only to uncover contextual information in each frame but also to directly capture inter-time dependencies.
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.
MeDaS: An open-source platform as service to help break the walls between medicine and informatics
To the best of our knowledge, MeDaS is the first open-source platform proving a collaborative and interactive service for researchers from a medical background easily using DL related toolkits, and at the same time for scientists or engineers from information sciences to understand the medical knowledge side.
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.
Efficient Scene Text Detection with Textual Attention Tower
Scene text detection has received attention for years and achieved an impressive performance across various benchmarks.
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.
