1 citations · 1 across the 6 of their papers we have counts for
6 papers
Parameter-efficient Dual-encoder Architecture with Differentiable Choquet Integral Fusion for Underwater Acoustic Classification
Amirmohammad Mohammadi, Joshua Peeples, Alexandra Van Dine
Underwater acoustic classification has a wide array of oceanic applications, but faces challenges due to an increasingly complex acoustic environment. Waveform and spectrogram repr…
Neighborhood Feature Pooling for Remote Sensing Image Classification
Fahimeh Orvati Nia, Amirmohammad Mohammadi, Salim Al Kharsa +3
In this work, we introduce Neighborhood Feature Pooling (NFP), a novel pooling layer designed to enhance texture-aware representation learning for remote sensing image classificati…
Histogram-based Parameter-efficient Tuning for Passive and Active Sonar Classification
Amirmohammad Mohammadi, Davelle Carreiro, Alexandra Van Dine +1
Parameter-efficient transfer learning (PETL) methods adapt large artificial neural networks to downstream tasks without fine-tuning the entire model. However, existing additive met…
Structural and Statistical Audio Texture Knowledge Distillation for Acoustic Classification
Jarin Ritu, Amirmohammad Mohammadi, Davelle Carreiro +2
While knowledge distillation has shown success in various audio tasks, its application to environmental sound classification often overlooks essential low-level audio texture featu…
Investigation of Time-Frequency Feature Combinations with Histogram Layer Time Delay Neural Networks
Amirmohammad Mohammadi, Iren'e Masabarakiza, Ethan Barnes +3
While deep learning has reduced the prevalence of manual feature extraction, transformation of data via feature engineering remains essential for improving model performance, parti…
Cross-Domain Knowledge Transfer for Underwater Acoustic Classification Using Pre-trained Models
Amirmohammad Mohammadi, Tejashri Kelhe, Davelle Carreiro +2
Transfer learning is commonly employed to leverage large, pre-trained models and perform fine-tuning for downstream tasks. The most prevalent pre-trained models are initially train…