activity
20172025
most citedDeep Multimodal Subspace Clustering Networks

182 citations · 222 across the 5 of their papers we have counts for

collaborators

6 papers

q-bio.GN2025

Context-Aware Regularization with Markovian Integration for Attention-Based Nucleotide Analysis

Mohammadsaleh Refahi, Mahdi Abavisani, Bahrad A. Sokhansanj +2

Transformers have revolutionized nucleotide sequence analysis, yet capturing long-range dependencies remains challenging. Recent studies show that autoregressive transformers often…

cs.LG2020★ 4 cited

Multimodal Categorization of Crisis Events in Social Media

Mahdi Abavisani, Liwei Wu, Shengli Hu +2

Recent developments in image classification and natural language processing, coupled with the rapid growth in social media usage, have enabled fundamental advances in detecting bre…

cs.CV2019★ 36 cited

Deep Sparse Representation-based Classification

Mahdi Abavisani, Vishal M. Patel

We present a transductive deep learning-based formulation for the sparse representation-based classification (SRC) method. The proposed network consists of a convolutional autoenco…

cs.CV2018

Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Training

Mahdi Abavisani, Hamid Reza Vaezi Joze, Vishal M. Patel

We present an efficient approach for leveraging the knowledge from multiple modalities in training unimodal 3D convolutional neural networks (3D-CNNs) for the task of dynamic hand…

cs.LG2018★ 182 cited

Deep Multimodal Subspace Clustering Networks

Mahdi Abavisani, Vishal M. Patel

We present convolutional neural network (CNN) based approaches for unsupervised multimodal subspace clustering. The proposed framework consists of three main stages - multimodal en…

cs.CV2017

In2I : Unsupervised Multi-Image-to-Image Translation Using Generative Adversarial Networks

Pramuditha Perera, Mahdi Abavisani, Vishal M. Patel

In unsupervised image-to-image translation, the goal is to learn the mapping between an input image and an output image using a set of unpaired training images. In this paper, we p…