74 citations · 248 across the 25 of their papers we have counts for
30 papers
Action Segmentation with Mixed Temporal Domain Adaptation
Min-Hung Chen, Baopu Li, Yingze Bao +1
The main progress for action segmentation comes from densely-annotated data for fully-supervised learning. Since manual annotation for frame-level actions is time-consuming and cha…
Joint Learning for Spatial Context-based Seismic Inversion of Multiple Datasets for Improved Generalizability and Robustness
Ahmad Mustafa, Motaz Alfarraj, Ghassan AlRegib
Seismic inversion plays a very useful role in detailed stratigraphic interpretation of seismic data. Seismic inversion enables estimation of rock properties over the complete seism…
Contrastive Reasoning in Neural Networks
Mohit Prabhushankar, Ghassan AlRegib
Neural networks represent data as projections on trained weights in a high dimensional manifold. The trained weights act as a knowledge base consisting of causal class dependencies…
Extracting Causal Visual Features for Limited label Classification
Mohit Prabhushankar, Ghassan AlRegib
Neural networks trained to classify images do so by identifying features that allow them to distinguish between classes. These sets of features are either causal or context depende…
Self-Supervised Annotation of Seismic Images using Latent Space Factorization
Oluwaseun Joseph Aribido, Ghassan AlRegib, Mohamed Deriche
Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation. In this paper,…
Gradients as a Measure of Uncertainty in Neural Networks
Jinsol Lee, Ghassan AlRegib
Despite tremendous success of modern neural networks, they are known to be overconfident even when the model encounters inputs with unfamiliar conditions. Detecting such inputs is…
On the Structures of Representation for the Robustness of Semantic Segmentation to Input Corruption
Charles Lehman, Dogancan Temel, Ghassan AlRegib
Semantic segmentation is a scene understanding task at the heart of safety-critical applications where robustness to corrupted inputs is essential. Implicit Background Estimation (…
Robustness and Overfitting Behavior of Implicit Background Models
Shirley Liu, Charles Lehman, Ghassan AlRegib
In this paper, we examine the overfitting behavior of image classification models modified with Implicit Background Estimation (SCrIBE), which transforms them into weakly supervise…
Novelty Detection Through Model-Based Characterization of Neural Networks
Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel +1
In this paper, we propose a model-based characterization of neural networks to detect novel input types and conditions. Novelty detection is crucial to identify abnormal inputs tha…
Implicit Saliency in Deep Neural Networks
Yutong Sun, Mohit Prabhushankar, Ghassan AlRegib
In this paper, we show that existing recognition and localization deep architectures, that have not been exposed to eye tracking data or any saliency datasets, are capable of predi…
Contrastive Explanations in Neural Networks
Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel +1
Visual explanations are logical arguments based on visual features that justify the predictions made by neural networks. Current modes of visual explanations answer questions of th…
Backpropagated Gradient Representations for Anomaly Detection
Gukyeong Kwon, Mohit Prabhushankar, Dogancan Temel +1
Learning representations that clearly distinguish between normal and abnormal data is key to the success of anomaly detection. Most of existing anomaly detection algorithms use act…
Joint Learning for Seismic Inversion: An Acoustic Impedance Estimation Case Study
Ahmad Mustafa, Ghassan AlRegib
Seismic inversion helps geophysicists build accurate reservoir models for exploration and production purposes. Deep learning-based seismic inversion works by training a neural netw…
Spatiotemporal Modeling of Seismic Images for Acoustic Impedance Estimation
Ahmad Mustafa, Motaz Alfarraj, Ghassan AlRegib
Seismic inversion refers to the process of estimating reservoir rock properties from seismic reflection data. Conventional and machine learning-based inversion workflows usually wo…
Fabric Surface Characterization: Assessment of Deep Learning-based Texture Representations Using a Challenging Dataset
Yuting Hu, Zhiling Long, Anirudha Sundaresan +4
Tactile sensing or fabric hand plays a critical role in an individual's decision to buy a certain fabric from the range of available fabrics for a desired application. Therefore, t…
Texture Classification using Block Intensity and Gradient Difference (BIGD) Descriptor
Yuting Hu, Zhen Wang, Ghassan AlRegib
In this paper, we present an efficient and distinctive local descriptor, namely block intensity and gradient difference (BIGD). In an image patch, we randomly sample multi-scale bl…
Estimation of Acoustic Impedance from Seismic Data using Temporal Convolutional Network
Ahmad Mustafa, Motaz Alfarraj, Ghassan AlRegib
In exploration seismology, seismic inversion refers to the process of inferring physical properties of the subsurface from seismic data. Knowledge of physical properties can prove…
Learning to Generate Grounded Visual Captions without Localization Supervision
Chih-Yao Ma, Yannis Kalantidis, Ghassan AlRegib +3
When automatically generating a sentence description for an image or video, it often remains unclear how well the generated caption is grounded, that is whether the model uses the…
Semi-supervised Learning for Acoustic Impedance Inversion
Motaz Alfarraj, Ghassan AlRegib
Recent applications of deep learning in the seismic domain have shown great potential in different areas such as inversion and interpretation. Deep learning algorithms, in general,…
Structure Label Prediction Using Similarity-Based Retrieval and Weakly-Supervised Label Mapping
Yazeed Alaudah, Motaz Alfarraj, Ghassan AlRegib
Recently, there has been significant interest in various supervised machine learning techniques that can help reduce the time and effort consumed by manual interpretation workflows…
3D Curvature Analysis of Seismic Waveforms and its Interpretational Implications
Haibin Di, Motaz Alfarraj, Ghassan AlRegib
The idea of curvature analysis has been widely used in subsurface structure interpretation from three-dimensional (3D) seismic data (e.g., fault/fracture detection and geomorpholog…
Multiscale Fusion for Seismic Geometric Attribute Enhancement
Motaz Alfarraj, Haibin Di, Ghassan AlRegib
In this abstract, we propose a multiscale fusion technique to enhance seismic geometric attributes, such as dip and curvature, which are very sensitive to noise present in seismic…
Multiresolution Analysis and Learning for Computational Seismic Interpretation
Motaz Alfarraj, Yazeed Alaudah, Zhiling Long +1
We explore the use of multiresolution analysis techniques as texture attributes for seismic image characterization, especially in representing subsurface structures in large migrat…
A Directional Coherence Attribute for Seismic Interpretation
Yazeed Alaudah, Ghassan AlRegib
The coherence attribute is one of the most commonly used attributes in seismic interpretation. In this paper, we propose building on the recently introduced Generalized Tensor-base…