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Indian Institute of Technology Roorkee

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Selected work

Representative Papers

Multiscaled Multi-Head Attention-Based Video Transformer Network for Hand Gesture Recognition

Jan 01, 2025IEEE Signal Processing Letters

Dynamic gesture recognition faces robustness bottlenecks due to inter-subject variations in pose, scale, and deformation. To address this, we propose the Multi-Scale Multi-Head Attention Video Transformer Network (MsMHA-VTN), a novel architecture featuring a pyramid-style multi-scale feature extraction module and the first multi-scale multi-head self-attention mechanism—where each attention head independently adapts to distinct spatiotemporal dimensions, enabling effective cross-scale temporal modeling. The model supports both unimodal (e.g., RGB) and multimodal (RGB-D) gesture recognition. Evaluated on NVGesture and Briareo benchmarks, MsMHA-VTN achieves state-of-the-art accuracy of 88.22% and 99.10%, respectively—substantially outperforming existing methods. These results demonstrate its effectiveness and strong generalization capability for complex dynamic sign language recognition under real-world variability.

13 citationsRead paper

Fast Risk Assessment in Power Grids through Novel Gaussian Process and Active Learning

Aug 15, 2023

To address the lack of formal performance guarantees for machine learning in safety-critical power system applications, this paper proposes a graph-structured Gaussian process (GP) method for voltage-constraint risk assessment. We introduce a novel vertex-degree kernel (VDK) that explicitly encodes topological dependencies between voltages and loads on the power grid, and design an active learning strategy aligned with the additive structure of VDK. Theoretically, we prove that the risk estimation error of the VDK-GP matches that of the AC power flow model and establish, for the first time, a statistically grounded probabilistic error bound for graph-based stochastic modeling. Evaluations on 500- and 1354-bus systems demonstrate over a twofold reduction in sample complexity, more than 15× speedup in computation versus Monte Carlo simulation, and risk estimation errors at the 10⁻⁴ level.

2 citationsRead paper
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