335 citations · 1.2k across the 81 of their papers we have counts for
34 papers · 1 filter
Physics-Informed Image Restoration via Progressive PDE Integration
Shamika Likhite, Santiago López-Tapia, Aggelos K. Katsaggelos
Motion blur, caused by relative movement between camera and scene during exposure, significantly degrades image quality and impairs downstream computer vision tasks such as object…
Caption-Driven Explainability: Probing CNNs for Bias via CLIP
Patrick Koller, Amil V. Dravid, Guido M. Schuster +1
Robustness has become one of the most critical problems in machine learning (ML). The science of interpreting ML models to understand their behavior and improve their robustness is…
THOR: Thermal-guided Hand-Object Reasoning via Adaptive Vision Sampling
Soroush Shahi, Farzad Shahabi, Rama Nabulsi +3
Wearable cameras are increasingly used as an observational and interventional tool for human behaviors by providing detailed visual data of hand-related activities. This data can b…
Sm: enhanced localization in Multiple Instance Learning for medical imaging classification
Francisco M. Castro-Macías, Pablo Morales-Álvarez, Yunan Wu +2
Multiple Instance Learning (MIL) is widely used in medical imaging classification to reduce the labeling effort. While only bag labels are available for training, one typically see…
VDPI: Video Deblurring with Pseudo-inverse Modeling
Zhihao Huang, Santiago Lopez-Tapia, Aggelos K. Katsaggelos
Video deblurring is a challenging task that aims to recover sharp sequences from blur and noisy observations. The image-formation model plays a crucial role in traditional model-ba…
Brighteye: Glaucoma Screening with Color Fundus Photographs based on Vision Transformer
Hui Lin, Charilaos Apostolidis, Aggelos K. Katsaggelos
Differences in image quality, lighting conditions, and patient demographics pose challenges to automated glaucoma detection from color fundus photography. Brighteye, a method based…
Cross-Temporal Spectrogram Autoencoder (CTSAE): Unsupervised Dimensionality Reduction for Clustering Gravitational Wave Glitches
Yi Li, Yunan Wu, Aggelos K. Katsaggelos
The advancement of The Laser Interferometer Gravitational-Wave Observatory (LIGO) has significantly enhanced the feasibility and reliability of gravitational wave detection. Howeve…
Focused Active Learning for Histopathological Image Classification
Arne Schmidt, Pablo Morales-Álvarez, Lee A. D. Cooper +4
Active Learning (AL) has the potential to solve a major problem of digital pathology: the efficient acquisition of labeled data for machine learning algorithms. However, existing A…
Explainable Transformer Prototypes for Medical Diagnoses
Ugur Demir, Debesh Jha, Zheyuan Zhang +4
Deployments of artificial intelligence in medical diagnostics mandate not just accuracy and efficacy but also trust, emphasizing the need for explainability in machine decisions. T…
Real-World Atmospheric Turbulence Correction via Domain Adaptation
Xijun Wang, Santiago López-Tapia, Aggelos K. Katsaggelos
Atmospheric turbulence, a common phenomenon in daily life, is primarily caused by the uneven heating of the Earth's surface. This phenomenon results in distorted and blurred acquir…
Atmospheric Turbulence Correction via Variational Deep Diffusion
Xijun Wang, Santiago López-Tapia, Aggelos K. Katsaggelos
Atmospheric Turbulence (AT) correction is a challenging restoration task as it consists of two distortions: geometric distortion and spatially variant blur. Diffusion models have s…
Video-Specific Query-Key Attention Modeling for Weakly-Supervised Temporal Action Localization
Xijun Wang, Aggelos K. Katsaggelos
Weakly-supervised temporal action localization aims to identify and localize the action instances in the untrimmed videos with only video-level action labels. When humans watch vid…
Thermal Spread Functions (TSF): Physics-guided Material Classification
Aniket Dashpute, Vishwanath Saragadam, Emma Alexander +4
Robust and non-destructive material classification is a challenging but crucial first-step in numerous vision applications. We propose a physics-guided material classification fram…
BKinD-3D: Self-Supervised 3D Keypoint Discovery from Multi-View Videos
Jennifer J. Sun, Lili Karashchuk, Amil Dravid +9
Quantifying motion in 3D is important for studying the behavior of humans and other animals, but manual pose annotations are expensive and time-consuming to obtain. Self-supervised…
Can Deep Learning Assist Automatic Identification of Layered Pigments From XRF Data?
Bingjie, Xu, Yunan Wu +12
X-ray fluorescence spectroscopy (XRF) plays an important role for elemental analysis in a wide range of scientific fields, especially in cultural heritage. XRF imaging, which uses…
Compressive Ptychography using Deep Image and Generative Priors
Semih Barutcu, Doğa Gürsoy, Aggelos K. Katsaggelos
Ptychography is a well-established coherent diffraction imaging technique that enables non-invasive imaging of samples at a nanometer scale. It has been extensively used in various…
medXGAN: Visual Explanations for Medical Classifiers through a Generative Latent Space
Amil Dravid, Florian Schiffers, Boqing Gong +1
Despite the surge of deep learning in the past decade, some users are skeptical to deploy these models in practice due to their black-box nature. Specifically, in the medical space…
Investigating the Potential of Auxiliary-Classifier GANs for Image Classification in Low Data Regimes
Amil Dravid, Florian Schiffers, Yunan Wu +2
Generative Adversarial Networks (GANs) have shown promise in augmenting datasets and boosting convolutional neural networks' (CNN) performance on image classification tasks. But th…
Visual Explanations for Convolutional Neural Networks via Latent Traversal of Generative Adversarial Networks
Amil Dravid, Aggelos K. Katsaggelos
Lack of explainability in artificial intelligence, specifically deep neural networks, remains a bottleneck for implementing models in practice. Popular techniques such as Gradient-…
Reinforcement Learning for Adaptive Video Compressive Sensing
Sidi Lu, Xin Yuan, Aggelos K Katsaggelos +1
We apply reinforcement learning to video compressive sensing to adapt the compression ratio. Specifically, video snapshot compressive imaging (SCI), which captures high-speed video…
Adaptive Illumination based Depth Sensing using Deep Superpixel and Soft Sampling Approximation
Qiqin Dai, Fengqiang Li, Oliver Cossairt +1
Dense depth map capture is challenging in existing active sparse illumination based depth acquisition techniques, such as LiDAR. Various techniques have been proposed to estimate a…
E3D: Event-Based 3D Shape Reconstruction
Alexis Baudron, Zihao W. Wang, Oliver Cossairt +1
3D shape reconstruction is a primary component of augmented/virtual reality. Despite being highly advanced, existing solutions based on RGB, RGB-D and Lidar sensors are power and d…
Lossy Event Compression based on Image-derived Quad Trees and Poisson Disk Sampling
Srutarshi Banerjee, Zihao W. Wang, Henry H. Chopp +2
With several advantages over conventional RGB cameras, event cameras have provided new opportunities for tackling visual tasks under challenging scenarios with fast motion, high dy…
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models
Santiago López-Tapia, Alice Lucas, Rafael Molina +1
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions. Current Video…