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Found 16 papers, 7 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.
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.
Content-Conditioned Generation of Stylized Free hand Sketches
We explore the performance of the model, including using styles randomly sampled from a prior normal distribution to generate images with various free-hand sketching styles, disentangling the painters' styles from known free-hand sketches to generate images with specific styles, and generating images of unknown classes that are not in the training set.
A multimodal gesture recognition dataset for desktop human-computer interaction
Gesture recognition is an indispensable component of natural and efficient human-computer interaction technology, particularly in desktop-level applications, where it can significantly enhance people's productivity.
Flowmind2Digital: The First Comprehensive Flowmind Recognition and Conversion Approach
Automated conversion methods are essential to overcome manual conversion challenges.
Enhance Sketch Recognition's Explainability via Semantic Component-Level Parsing
Humans can recognize varied sketches of a category easily by identifying the concurrence and layout of the intrinsic semantic components of the category, since humans draw free-hand sketches based a common consensus that which types of semantic components constitute each sketch category.
Sketch Input Method Editor: A Comprehensive Dataset and Methodology for Systematic Input Recognition
With the recent surge in the use of touchscreen devices, free-hand sketching has emerged as a promising modality for human-computer interaction.
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.
Free-form Description Guided 3D Visual Graph Network for Object Grounding in Point Cloud
There are three main challenges in 3D object grounding: to find the main focus in the complex and diverse description; to understand the point cloud scene; and to locate the target object.
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.
