8 papers
TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition
Yanan Liu, Jun Liu, Hao Zhang +4
Skeleton-based action recognition has garnered significant attention in the computer vision community. Inspired by the recent success of the selective state-space model (SSM) Mamba…
Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation
Jingmin Zhu, Anqi Zhu, Hossein Rahmani +3
We introduce Skeleton-Cache, the first training-free test-time adaptation framework for skeleton-based zero-shot action recognition (SZAR), aimed at improving model generalization…
Neuromorphic Astronomy: An End-to-End SNN Pipeline for RFI Detection Hardware
Nicholas J. Pritchard, Andreas Wicenec, Richard Dodson +2
Imminent radio telescope observatories provide massive data rates making deep learning based processing appealing while simultaneously demanding real-time performance at low-energy…
Spiking Neural Networks for Radio Frequency Interference Detection in Radio Astronomy
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun +1
Spiking Neural Networks (SNNs) promise efficient and dynamic spatio-temporal data processing. This paper reformulates a significant challenge in radio astronomy, Radio Frequency In…
Polarisation-Inclusive Spiking Neural Networks for Real-Time RFI Detection in Modern Radio Telescopes
Nicholas J. Pritchard, Andreas Wicenec, Richard Dodson +1
Radio Frequency Interference (RFI) is a known growing challenge for radio astronomy, intensified by increasing observatory sensitivity and prevalence of orbital RFI sources. Spikin…
Admitting Ignorance Helps the Video Question Answering Models to Answer
Haopeng Li, Tom Drummond, Mingming Gong +2
Significant progress has been made in the field of video question answering (VideoQA) thanks to deep learning and large-scale pretraining. Despite the presence of sophisticated mod…