205 citations · 759 across the 46 of their papers we have counts for
48 papers
Learning Time-Series Representations by Hierarchical Uniformity-Tolerance Latent Balancing
Amin Jalali, Milad Soltany, Michael Greenspan +1
We propose TimeHUT, a novel method for learning time-series representations by hierarchical uniformity-tolerance balancing of contrastive representations. Our method uses two disti…
PRISM: Reducing Spurious Implicit Biases in Vision-Language Models with LLM-Guided Embedding Projection
Mahdiyar Molahasani, Azadeh Motamedi, Michael Greenspan +2
We introduce Projection-based Reduction of Implicit Spurious bias in vision-language Models (PRISM), a new data-free and task-agnostic solution for bias mitigation in VLMs like CLI…
Socially-Informed Reconstruction for Pedestrian Trajectory Forecasting
Haleh Damirchi, Ali Etemad, Michael Greenspan
Pedestrian trajectory prediction remains a challenge for autonomous systems, particularly due to the intricate dynamics of social interactions. Accurate forecasting requires a comp…
Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training
Milad Soltany, Farhad Pourpanah, Mahdiyar Molahasani +2
In this paper, we propose a novel approach, Federated Domain Generalization with Label Smoothing and Balanced Decentralized Training (FedSB), to address the challenges of data hete…
CycleCrash: A Dataset of Bicycle Collision Videos for Collision Prediction and Analysis
Nishq Poorav Desai, Ali Etemad, Michael Greenspan
Self-driving research often underrepresents cyclist collisions and safety. To address this, we present CycleCrash, a novel dataset consisting of 3,000 dashcam videos with 436,347 f…
NapTune: Efficient Model Tuning for Mood Classification using Previous Night's Sleep Measures along with Wearable Time-series
Debaditya Shome, Nasim Montazeri Ghahjaverestan, Ali Etemad
Sleep is known to be a key factor in emotional regulation and overall mental health. In this study, we explore the integration of sleep measures from the previous night into wearab…
Federated Unsupervised Domain Generalization using Global and Local Alignment of Gradients
Farhad Pourpanah, Mahdiyar Molahasani, Milad Soltany +2
We address the problem of federated domain generalization in an unsupervised setting for the first time. We first theoretically establish a connection between domain shift and alig…
Remote Heart Rate Monitoring in Smart Environments from Videos with Self-supervised Pre-training
Divij Gupta, Ali Etemad
Recent advances in deep learning have made it increasingly feasible to estimate heart rate remotely in smart environments by analyzing videos. However, a notable limitation of deep…
Diffusion Models with Deterministic Normalizing Flow Priors
Mohsen Zand, Ali Etemad, Michael Greenspan
For faster sampling and higher sample quality, we propose DiNof (ffusion with rmalizing low priors), a technique that makes use of normalizing…
Multiscale Residual Learning of Graph Convolutional Sequence Chunks for Human Motion Prediction
Mohsen Zand, Ali Etemad, Michael Greenspan
A new method is proposed for human motion prediction by learning temporal and spatial dependencies. Recently, multiscale graphs have been developed to model the human body at highe…
EEG-based Cognitive Load Classification using Feature Masked Autoencoding and Emotion Transfer Learning
Dustin Pulver, Prithila Angkan, Paul Hungler +1
Cognitive load, the amount of mental effort required for task completion, plays an important role in performance and decision-making outcomes, making its classification and analysi…
Context-aware Pedestrian Trajectory Prediction with Multimodal Transformer
Haleh Damirchi, Michael Greenspan, Ali Etemad
We propose a novel solution for predicting future trajectories of pedestrians. Our method uses a multimodal encoder-decoder transformer architecture, which takes as input both pede…
Continual Learning for Out-of-Distribution Pedestrian Detection
Mahdiyar Molahasani, Ali Etemad, Michael Greenspan
A continual learning solution is proposed to address the out-of-distribution generalization problem for pedestrian detection. While recent pedestrian detection models have achieved…
Toward Fair Facial Expression Recognition with Improved Distribution Alignment
Mojtaba Kolahdouzi, Ali Etemad
We present a novel approach to mitigate bias in facial expression recognition (FER) models. Our method aims to reduce sensitive attribute information such as gender, age, or race,…
On The Relationship Between Continual Learning and Long-Tailed Recognition
Mahdiyar Molahasani, Michael Greenspan, Ali Etemad
Real-world datasets often exhibit long-tailed distributions, where a few dominant "Head" classes have abundant samples while most "Tail" classes are severely underrepresented, lead…
Privacy-Preserving Remote Heart Rate Estimation from Facial Videos
Divij Gupta, Ali Etemad
Remote Photoplethysmography (rPPG) is the process of estimating PPG from facial videos. While this approach benefits from contactless interaction, it is reliant on videos of faces,…
In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection
Sahar Soltanieh, Javad Hashemi, Ali Etemad
This paper presents a systematic investigation into the effectiveness of Self-Supervised Learning (SSL) methods for Electrocardiogram (ECG) arrhythmia detection. We begin by conduc…
Multimodal Brain-Computer Interface for In-Vehicle Driver Cognitive Load Measurement: Dataset and Baselines
Prithila Angkan, Behnam Behinaein, Zunayed Mahmud +4
Through this paper, we introduce a novel driver cognitive load assessment dataset, CL-Drive, which contains Electroencephalogram (EEG) signals along with other physiological signal…
Partial Label Learning for Emotion Recognition from EEG
Guangyi Zhang, Ali Etemad
Fully supervised learning has recently achieved promising performance in various electroencephalography (EEG) learning tasks by training on large datasets with ground truth labels.…
FaceTopoNet: Facial Expression Recognition using Face Topology Learning
Mojtaba Kolahdouzi, Alireza Sepas-Moghaddam, Ali Etemad
Prior work has shown that the order in which different components of the face are learned using a sequential learner can play an important role in the performance of facial express…
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning
Setareh Rahimi Taghanaki, Michael Rainbow, Ali Etemad
In this paper, we propose a self-supervised learning solution for human activity recognition with smartphone accelerometer data. We aim to develop a model that learns strong repres…
Multimodal Estimation of End Point Force During Quasi-dynamic and Dynamic Muscle Contractions Using Deep Learning
Gelareh Hajian, Evelyn Morin, Ali Etemad
Accurate force/torque estimation is essential for applications such as powered exoskeletons, robotics, and rehabilitation. However, force/torque estimation under dynamic conditions…
ObjectBox: From Centers to Boxes for Anchor-Free Object Detection
Mohsen Zand, Ali Etemad, Michael Greenspan
We present ObjectBox, a novel single-stage anchor-free and highly generalizable object detection approach. As opposed to both existing anchor-based and anchor-free detectors, which…
Learning Sequential Contexts using Transformer for 3D Hand Pose Estimation
Leyla Khaleghi, Joshua Marshall, Ali Etemad
3D hand pose estimation (HPE) is the process of locating the joints of the hand in 3D from any visual input. HPE has recently received an increased amount of attention due to its k…