17 citations · 20 across the 6 of their papers we have counts for
6 papers
DEL: Dense Event Localization for Multi-modal Audio-Visual Understanding
Mona Ahmadian, Amir Shirian, Frank Guerin +1
Real-world videos often contain overlapping events and complex temporal dependencies, making multimodal interaction modeling particularly challenging. We introduce DEL, a framework…
SAFE: Saliency-Aware Counterfactual Explanations for DNN-based Automated Driving Systems
Amir Samadi, Amir Shirian, Konstantinos Koufos +2
A CF explainer identifies the minimum modifications in the input that would alter the model's output to its complement. In other words, a CF explainer computes the minimum modifica…
Heterogeneous Graph Learning for Acoustic Event Classification
Amir Shirian, Mona Ahmadian, Krishna Somandepalli +1
Heterogeneous graphs provide a compact, efficient, and scalable way to model data involving multiple disparate modalities. This makes modeling audiovisual data using heterogeneous…
Visually-aware Acoustic Event Detection using Heterogeneous Graphs
Amir Shirian, Krishna Somandepalli, Victor Sanchez +1
Perception of auditory events is inherently multimodal relying on both audio and visual cues. A large number of existing multimodal approaches process each modality using modality-…
Self-supervised Graphs for Audio Representation Learning with Limited Labeled Data
Amir Shirian, Krishna Somandepalli, Tanaya Guha
Large scale databases with high-quality manual annotations are scarce in audio domain. We thus explore a self-supervised graph approach to learning audio representations from highl…
Compact Graph Architecture for Speech Emotion Recognition
A. Shirian, T. Guha
We propose a deep graph approach to address the task of speech emotion recognition. A compact, efficient and scalable way to represent data is in the form of graphs. Following the…