Sarthak Kumar Maharana — accepted AI-conference papers

2025

$\texttt{AVROBUSTBENCH}$: Benchmarking the Robustness of Audio-Visual Recognition Models at Test-Time

Sarthak Kumar Maharana, Saksham Singh Kushwaha, Baoming Zhang, Adrian Rodriguez, Songtao Wei, Yapeng Tian +1

NeurIPS 2025poster

While recent audio-visual models have demonstrated impressive performance, their robustness to distributional shifts at test-time remains not fully understood. Existing robustness benchmarks mainly focus on single modalities, making them insufficient for thoroughly assessing the robustness of audio-…

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2025

PALM: Pushing Adaptive Learning Rate Mechanisms for Continual Test-Time Adaptation

Sarthak Kumar Maharana, Baoming Zhang, Yunhui Guo

AAAI 2025technical

Real-world vision models in dynamic environments face rapid shifts in domain distributions, leading to decreased recognition performance. Using unlabeled test data, continuous test-time adaptation (CTTA) directly adjusts a pre-trained source discriminative model to these changing domains. A highly e…

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2024

"Not Just Change the Labels, Learn the Features: Watermarking Deep Neural Networks with Multi-View Data"

Yuxuan Li, Sarthak Kumar Maharana, Yunhui Guo*

ECCV 2024poster

"With the increasing prevalence of Machine Learning as a Service (MLaaS) platforms, there is a growing focus on deep neural network (DNN) watermarking techniques. These methods are used to facilitate the verification of ownership for a target DNN model to protect intellectual property. One of the mo…

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2024

STONE: A Submodular Optimization Framework for Active 3D Object Detection

RUIYU MAO, Sarthak Kumar Maharana, Rishabh K Iyer, Yunhui Guo

NeurIPS 2024poster

3D object detection is fundamentally important for various emerging applications, including autonomous driving and robotics. A key requirement for training an accurate 3D object detector is the availability of a large amount of LiDAR-based point cloud data. Unfortunately, labeling point cloud data i…

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2021

Acoustic-to-Articulatory Inversion for Dysarthric Speech by Using Cross-Corpus Acoustic-Articulatory Data

Sarthak Kumar Maharana, Aravind Illa, Renuka Mannem, Yamini Belur, Preetie Shetty, Preethish-Kumar Veeramani +4

ICASSP 2021accepted

In this work, we focus on estimating articulatory movements from acoustic features, known as acoustic-to-articulatory inversion (AAI), for dysarthric patients with amyotrophic lateral sclerosis (ALS). Unlike healthy subjects, there are two potential challenges involved in AAI on dysarthric speech. D…

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