16 citations · 21 across the 10 of their papers we have counts for
11 papers
Nanoscale chemical engineering of Ti3C2Tx MXene multilayers through in situ electron beam irradiation in a TEM
Ayoub Benmoumen, Marie-Laure David, Noé Condé +7
Defect engineering in low-dimensional materials is a powerful approach to tailor their structure and chemistry at the atomic scale, with high relevance for tuning physical properti…
TORINO: Token Reduction via Interpretable Concept Overlap in Vision-Language Models
Riccardo Renzulli, Gabriele Spadaro, Shruthi Gowda +2
Vision-Language Models (VLMs) have demonstrated impressive capabilities across different tasks, but their computational cost is dominated by the large number of visual tokens fed t…
Is Our Benchmark Enough? An Analysis of Continual Learning for MLLMs
Van-Tuan Tran, Shruthi Gowda, Merim Dzaferagic +1
Continual adaptation is essential for multimodal large language models (MLLMs) deployed across evolving domains, but the state-of-the-art MR-LoRA method highly relies on the assump…
Can We Break Free from Strong Data Augmentations in Self-Supervised Learning?
Shruthi Gowda, Elahe Arani, Bahram Zonooz
Self-supervised learning (SSL) has emerged as a promising solution for addressing the challenge of limited labeled data in deep neural networks (DNNs), offering scalability potenti…
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training
Shruthi Gowda, Bahram Zonooz, Elahe Arani
Adversarial training improves the robustness of neural networks against adversarial attacks, albeit at the expense of the trade-off between standard and robust generalization. To u…
Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning
Shruthi Gowda, Bahram Zonooz, Elahe Arani
Artificial neural networks (ANNs) exhibit a narrow scope of expertise on stationary independent data. However, the data in the real world is continuous and dynamic, and ANNs must a…
LSFSL: Leveraging Shape Information in Few-shot Learning
Deepan Chakravarthi Padmanabhan, Shruthi Gowda, Elahe Arani +1
Few-shot learning (FSL) techniques seek to learn the underlying patterns in data using fewer samples, analogous to how humans learn from limited experience. In this limited-data sc…
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey
Elahe Arani, Shruthi Gowda, Ratnajit Mukherjee +3
Deep neural network based object detectors are continuously evolving and are used in a multitude of applications, each having its own set of requirements. While safety-critical app…
InBiaseD: Inductive Bias Distillation to Improve Generalization and Robustness through Shape-awareness
Shruthi Gowda, Bahram Zonooz, Elahe Arani
Humans rely less on spurious correlations and trivial cues, such as texture, compared to deep neural networks which lead to better generalization and robustness. It can be attribut…
Does Thermal data make the detection systems more reliable?
Shruthi Gowda, Bahram Zonooz, Elahe Arani
Deep learning-based detection networks have made remarkable progress in autonomous driving systems (ADS). ADS should have reliable performance across a variety of ambient lighting…
AI Driven Road Maintenance Inspection
Ratnajit Mukherjee, Haris Iqbal, Shabbir Marzban +5
Road infrastructure maintenance inspection is typically a labour-intensive and critical task to ensure the safety of all the road users. In this work, we propose a detailed methodo…