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Found 16 papers, 0 papers with code
Learning Through Guidance: Knowledge Distillation for Endoscopic Image Classification
However, there is a lack of focus on developing lightweight models which can run in low-resource environments, which are typically encountered in medical clinics.
Remembering What Is Important: A Factorised Multi-Head Retrieval and Auxiliary Memory Stabilisation Scheme for Human Motion Prediction
Humans exhibit complex motions that vary depending on the task that they are performing, the interactions they engage in, as well as subject-specific preferences.
Vision-Based Activity Recognition in Children with Autism-Related Behaviors
Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting.
Towards On-Board Panoptic Segmentation of Multispectral Satellite Images
To address this challenge, we propose a multimodal teacher network based on a cross-modality attention-based fusion strategy to improve the segmentation accuracy by exploiting data from multiple modes.
Continuous Human Action Recognition for Human-Machine Interaction: A Review
With advances in data-driven machine learning research, a wide variety of prediction models have been proposed to capture spatio-temporal features for the analysis of video streams.
Multi-Slice Net: A novel light weight framework for COVID-19 Diagnosis
This paper presents a novel lightweight COVID-19 diagnosis framework using CT scans.
Deep Learning for Medical Anomaly Detection -- A Survey
Machine learning-based medical anomaly detection is an important problem that has been extensively studied.
Multi-modal Fusion for Single-Stage Continuous Gesture Recognition
Gesture recognition is a much studied research area which has myriad real-world applications including robotics and human-machine interaction.
Two-Stream Deep Feature Modelling for Automated Video Endoscopy Data Analysis
Automating the analysis of imagery of the Gastrointestinal (GI) tract captured during endoscopy procedures has substantial potential benefits for patients, as it can provide diagnostic support to medical practitioners and reduce mistakes via human error.
Hierarchical Attention Network for Action Segmentation
The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video.
Predicting the Future: A Jointly Learnt Model for Action Anticipation
Inspired by human neurological structures for action anticipation, we present an action anticipation model that enables the prediction of plausible future actions by forecasting both the visual and temporal future.
Forecasting Future Action Sequences with Neural Memory Networks
We propose a novel neural memory network based framework for future action sequence forecasting.
Fine-grained Action Segmentation using the Semi-Supervised Action GAN
In this paper we address the problem of continuous fine-grained action segmentation, in which multiple actions are present in an unsegmented video stream.
Coupled Generative Adversarial Network for Continuous Fine-grained Action Segmentation
The goal of both GANs is to generate similar `action codes', a vector representation of the current action.
Multi-Level Sequence GAN for Group Activity Recognition
The generator is fed with person-level and scene-level features that are mapped temporally through LSTM networks.
Two Stream LSTM: A Deep Fusion Framework for Human Action Recognition
Our contribution in this paper is a deep fusion framework that more effectively exploits spatial features from CNNs with temporal features from LSTM models.
