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Found 37 papers, 1 papers with code
Cross-Branch Orthogonality for Improved Generalization in Face Deepfake Detection
Remarkable advancements in generative AI technology have given rise to a spectrum of novel deepfake categories with unprecedented leaps in their realism, and deepfakes are increasingly becoming a nuisance to law enforcement authorities and the general public.
Dual-Domain Masked Image Modeling: A Self-Supervised Pretraining Strategy Using Spatial and Frequency Domain Masking for Hyperspectral Data
In spatial masking, we randomly mask selected patches and train the model to reconstruct the masked inputs using the visible patches.
Spectral-Enhanced Transformers: Leveraging Large-Scale Pretrained Models for Hyperspectral Object Tracking
This is particularly critical for complex tasks like object tracking, and the scarcity of large datasets in the hyperspectral domain acts as a bottleneck in achieving the full potential of powerful transformer models.
Face Deepfakes - A Comprehensive Review
In recent years, remarkable advancements in deep- fake generation technology have led to unprecedented leaps in its realism and capabilities.
Damage Assessment after Natural Disasters with UAVs: Semantic Feature Extraction using Deep Learning
The semantic extractor can be executed onboard which results in a reduction of data that needs to be transmitted to ground stations.
Physics Augmented Tuple Transformer for Autism Severity Level Detection
This paper proposes a novel framework that exploits the laws of physics for ASD severity recognition.
PseudoNeg-MAE: Self-Supervised Point Cloud Learning using Conditional Pseudo-Negative Embeddings
We propose PseudoNeg-MAE, a novel self-supervised learning framework that enhances global feature representation of point cloud mask autoencoder by making them both discriminative and sensitive to transformations.
AI, Entrepreneurs, and Privacy: Deep Learning Outperforms Humans in Detecting Entrepreneurs from Image Data
In this study, we demonstrate that deep neural networks can classify individuals as entrepreneurs with high accuracy based on facial images sourced from Crunchbase, a premier source for entrepreneurship data.
FactoFormer: Factorized Hyperspectral Transformers with Self-Supervised Pretraining
However, current state-of-the-art hyperspectral transformers only tokenize the input HSI sample along the spectral dimension, resulting in the under-utilization of spatial information.
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.
Ranked #3 on
Human Pose Forecasting
on Human3.6M
Physical Adversarial Attacks for Surveillance: A Survey
In particular, we propose a framework to analyze physical adversarial attacks and provide a comprehensive survey of physical adversarial attacks on four key surveillance tasks: detection, identification, tracking, and action recognition under this framework.
Using Auxiliary Information for Person Re-Identification -- A Tutorial Overview
Person re-identification (re-id) is a pivotal task within an intelligent surveillance pipeline and there exist numerous re-id frameworks that achieve satisfactory performance in challenging benchmarks.
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.
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.
Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings
We exceed the state-of-the-art results in all evaluations.
Deep Learning for Medical Anomaly Detection -- A Survey
Machine learning-based medical anomaly detection is an important problem that has been extensively studied.
Patient-independent Epileptic Seizure Prediction using Deep Learning Models
Patient-independent seizure prediction models are designed to offer accurate performance across multiple subjects within a dataset, and have been identified as a real-world solution to the seizure prediction problem.
Domain Generalization in Biosignal Classification
Conclusion: Recognizing the complexity induced by the inherent temporal nature of biosignal data, the two-stage method proposed in this study is able to effectively simplify the whole process of domain generalization while demonstrating good results on unseen domains and the adopted basis domains.
Fast & Slow Learning: Incorporating Synthetic Gradients in Neural Memory Controllers
In addition, we demonstrate the practical implications of the proposed learning strategy, where the feedback path can be shared among multiple neural memory networks as a mechanism for knowledge sharing.
Attention Driven Fusion for Multi-Modal Emotion Recognition
In this paper, we present a deep learning-based approach to exploit and fuse text and acoustic data for emotion classification.
Memory based fusion for multi-modal deep learning
The use of multi-modal data for deep machine learning has shown promise when compared to uni-modal approaches with fusion of multi-modal features resulting in improved performance in several applications.
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation
In this study, we explicitly examine the importance of heart sound segmentation as a prior step for heart sound classification, and then seek to apply the obtained insights to propose a robust classifier for abnormal heart sound detection.
Heart Sound Segmentation using Bidirectional LSTMs with Attention
This paper proposes a novel framework for the segmentation of phonocardiogram (PCG) signals into heart states, exploiting the temporal evolution of the PCG as well as considering the salient information that it provides for the detection of the heart state.
Temporarily-Aware Context Modelling using Generative Adversarial Networks for Speech Activity Detection
This paper presents a novel framework for Speech Activity Detection (SAD).
Neural Memory Networks for Seizure Type Classification
Inspired by recent advances in neural memory networks (NMNs), we introduce a novel approach for the classification of seizure type using electrophysiological data.
Exploiting Human Social Cognition for the Detection of Fake and Fraudulent Faces via Memory Networks
Advances in computer vision have brought us to the point where we have the ability to synthesise realistic fake content.
Neural Memory Plasticity for Anomaly Detection
In the domain of machine learning, Neural Memory Networks (NMNs) have recently achieved impressive results in a variety of application areas including visual question answering, trajectory prediction, object tracking, and language modelling.
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.
Memory Augmented Deep Generative models for Forecasting the Next Shot Location in Tennis
This paper presents a novel framework for predicting shot location and type in tennis.
GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds
This paper presents a novel deep learning framework for human trajectory prediction and detecting social group membership in crowds.
Pedestrian Trajectory Prediction with Structured Memory Hierarchies
This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar).
Deep Decision Trees for Discriminative Dictionary Learning with Adversarial Multi-Agent Trajectories
With the explosion in the availability of spatio-temporal tracking data in modern sports, there is an enormous opportunity to better analyse, learn and predict important events in adversarial group environments.
Learning Temporal Strategic Relationships using Generative Adversarial Imitation Learning
This paper presents a novel framework for automatic learning of complex strategies in human decision making.
Tracking by Prediction: A Deep Generative Model for Mutli-Person localisation and Tracking
We present a novel, complete deep learning framework for multi-person localisation and tracking.
Generative Adversarial Network
Pedestrian Trajectory Prediction
+2
Task Specific Visual Saliency Prediction with Memory Augmented Conditional Generative Adversarial Networks
Visual saliency patterns are the result of a variety of factors aside from the image being parsed, however existing approaches have ignored these.
Tree Memory Networks for Modelling Long-term Temporal Dependencies
In this paper, we propose a Tree Memory Network (TMN) for modelling long term and short term relationships in sequence-to-sequence mapping problems.
Soft + Hardwired Attention: An LSTM Framework for Human Trajectory Prediction and Abnormal Event Detection
We illustrate how a simple approximation of attention weights (i. e hard-wired) can be merged together with soft attention weights in order to make our model applicable for challenging real world scenarios with hundreds of neighbours.
