150 citations · 187 across the 24 of their papers we have counts for
26 papers
Machine Learning under Imperfect Data: Challenges and Methods
Masoumeh Zareapoor
Machine-learning models are commonly developed under an assumption that training and test data are sufficiently complete, balanced, labelled, and drawn from compatible distribution…
Task Switching Without Forgetting via Proximal Decoupling
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +2
In continual learning, the primary challenge is to learn new information without forgetting old knowledge. A common solution addresses this trade-off through regularization, penali…
Multi-Domain Learning with Global Expert Mapping
Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +3
Human perception generalizes well across different domains, but most vision models struggle beyond their training data. This gap motivates multi-dataset learning, where a single mo…
HMR-Net: Hierarchical Modular Routing for Cross-Domain Object Detection in Aerial Images
Pourya Shamsolmoali, Masoumeh Zareapoor, Michael Felsberg +3
Despite advances in object detection, aerial imagery remains a challenging domain, as models often fail to generalize across variations in spatial resolution, scene composition, an…
IntRec: Intent-based Retrieval with Contrastive Refinement
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +1
Retrieving user-specified objects from complex scenes remains a challenging task, especially when queries are ambiguous or involve multiple similar objects. Existing open-vocabular…
Finding Structure in Continual Learning
Pourya Shamsolmoali, Masoumeh Zareapoor
Learning from a stream of tasks usually pits plasticity against stability: acquiring new knowledge often causes catastrophic forgetting of past information. Most methods address th…
From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion
Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +3
Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have…
Fractional Correspondence Framework in Detection Transformer
Masoumeh Zareapoor, Pourya Shamsolmoali, Huiyu Zhou +2
The Detection Transformer (DETR), by incorporating the Hungarian algorithm, has significantly simplified the matching process in object detection tasks. This algorithm facilitates…
SeTformer is What You Need for Vision and Language
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +1
The dot product self-attention (DPSA) is a fundamental component of transformers. However, scaling them to long sequences, like documents or high-resolution images, becomes prohibi…
Distance Weighted Trans Network for Image Completion
Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +2
The challenge of image generation has been effectively modeled as a problem of structure priors or transformation. However, existing models have unsatisfactory performance in under…
ClusVPR: Efficient Visual Place Recognition with Clustering-based Weighted Transformer
Yifan Xu, Pourya Shamsolmoali, Jie Yang
Visual place recognition (VPR) is a highly challenging task that has a wide range of applications, including robot navigation and self-driving vehicles. VPR is particularly difficu…
Entropy Transformer Networks: A Learning Approach via Tangent Bundle Data Manifold
Pourya Shamsolmoali, Masoumeh Zareapoor
This paper focuses on an accurate and fast interpolation approach for image transformation employed in the design of CNN architectures. Standard Spatial Transformer Networks (STNs)…
Image Completion via Dual-path Cooperative Filtering
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger
Given the recent advances with image-generating algorithms, deep image completion methods have made significant progress. However, state-of-art methods typically provide poor cross…
VTAE: Variational Transformer Autoencoder with Manifolds Learning
Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +2
Deep generative models have demonstrated successful applications in learning non-linear data distributions through a number of latent variables and these models use a nonlinear fun…
Hybrid Gromov-Wasserstein Embedding for Capsule Learning
Pourya Shamsolmoali, Masoumeh Zareapoor, Swagatam Das +2
Capsule networks (CapsNets) aim to parse images into a hierarchy of objects, parts, and their relations using a two-step process involving part-whole transformation and hierarchica…
Enhanced Single-shot Detector for Small Object Detection in Remote Sensing Images
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +2
Small-object detection is a challenging problem. In the last few years, the convolution neural networks methods have been achieved considerable progress. However, the current detec…
Salient Skin Lesion Segmentation via Dilated Scale-Wise Feature Fusion Network
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +1
Skin lesion detection in dermoscopic images is essential in the accurate and early diagnosis of skin cancer by a computerized apparatus. Current skin lesion segmentation approaches…
Anchor Graph Structure Fusion Hashing for Cross-Modal Similarity Search
Lu Wang, Jie Yang, Masoumeh Zareapoor +1
Cross-modal hashing still has some challenges needed to address: (1) most existing CMH methods take graphs as input to model data distribution. These methods omit to consider the c…
Multi-patch Feature Pyramid Network for Weakly Supervised Object Detection in Optical Remote Sensing Images
Pourya Shamsolmoali, Jocelyn Chanussot, Masoumeh Zareapoor +2
Object detection is a challenging task in remote sensing because objects only occupy a few pixels in the images, and the models are required to simultaneously learn object location…
Rotation Equivariant Feature Image Pyramid Network for Object Detection in Optical Remote Sensing Imagery
Pourya Shamsolmoali, Masoumeh Zareapoor, Jocelyn Chanussot +2
Detection of objects is extremely important in various aerial vision-based applications. Over the last few years, the methods based on convolution neural networks have made substan…
Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies
Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger +4
Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, t…
Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
Pourya Shamsolmoali, Masoumeh Zareapoor, Huiyu Zhou +2
Road extraction in remote sensing images is of great importance for a wide range of applications. Because of the complex background, and high density, most of the existing methods…
Oversampling Adversarial Network for Class-Imbalanced Fault Diagnosis
Masoumeh Zareapoor, Pourya Shamsolmoali, Jie Yang
The collected data from industrial machines are often imbalanced, which poses a negative effect on learning algorithms. However, this problem becomes more challenging for a mixed t…
Imbalanced Data Learning by Minority Class Augmentation using Capsule Adversarial Networks
Pourya Shamsolmoali, Masoumeh Zareapoor, Linlin Shen +2
The fact that image datasets are often imbalanced poses an intense challenge for deep learning techniques. In this paper, we propose a method to restore the balance in imbalanced i…