3 citations · 5 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…