599 citations · 604 across the 10 of their papers we have counts for
12 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…
Advancing RFI-Detection in Radio Astronomy with Liquid State Machines
Nicholas J Pritchard, Andreas Wicenec, Mohammed Bennamoun +1
Radio Frequency Interference (RFI) from anthropogenic radio sources poses significant challenges to current and future radio telescopes. Contemporary approaches to detecting RFI tr…
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…
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…
Supervised Radio Frequency Interference Detection with SNNs
Nicholas J. Pritchard, Andreas Wicenec, Mohammed Bennamoun +1
Radio Frequency Interference (RFI) poses a significant challenge in radio astronomy, arising from terrestrial and celestial sources, disrupting observations conducted by radio tele…
Semantically-aware Neural Radiance Fields for Visual Scene Understanding: A Comprehensive Review
Thang-Anh-Quan Nguyen, Amine Bourki, Mátyás Macudzinski +2
This review thoroughly examines the role of semantically-aware Neural Radiance Fields (NeRFs) in visual scene understanding, covering an analysis of over 250 scholarly papers. It e…
Answering from Sure to Uncertain: Uncertainty-Aware Curriculum Learning for Video Question Answering
Haopeng Li, Mohammed Bennamoun, Jun Liu +2
While significant advancements have been made in video question answering (VideoQA), the potential benefits of enhancing model generalization through tailored difficulty scheduling…
Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports
Haopeng Li, Andong Deng, Jun Liu +5
Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not b…
Human Action Recognition from Various Data Modalities: A Review
Zehua Sun, Qiuhong Ke, Hossein Rahmani +3
Human Action Recognition (HAR) aims to understand human behavior and assign a label to each action. It has a wide range of applications, and therefore has been attracting increasin…