4 citations · 8 across the 5 of their papers we have counts for
5 papers
PLOT-TAL: Prompt Learning with Optimal Transport for Few-Shot Temporal Action Localization
Edward Fish, Andrew Gilbert
Few-shot temporal action localization (TAL) methods that adapt large models via single-prompt tuning often fail to produce precise temporal boundaries. This stems from the model le…
Multi-Resolution Audio-Visual Feature Fusion for Temporal Action Localization
Edward Fish, Jon Weinbren, Andrew Gilbert
Temporal Action Localization (TAL) aims to identify actions' start, end, and class labels in untrimmed videos. While recent advancements using transformer networks and Feature Pyra…
A Model for Every User and Budget: Label-Free and Personalized Mixed-Precision Quantization
Edward Fish, Umberto Michieli, Mete Ozay
Recent advancement in Automatic Speech Recognition (ASR) has produced large AI models, which become impractical for deployment in mobile devices. Model quantization is effective to…
Two-Stream Transformer Architecture for Long Video Understanding
Edward Fish, Jon Weinbren, Andrew Gilbert
Pure vision transformer architectures are highly effective for short video classification and action recognition tasks. However, due to the quadratic complexity of self attention a…
Rethinking movie genre classification with fine-grained semantic clustering
Edward Fish, Jon Weinbren, Andrew Gilbert
Movie genre classification is an active research area in machine learning. However, due to the limited labels available, there can be large semantic variations between movies withi…