Attributes-Aware Network for Temporal Action Detection
Rui Dai (INRIA),* Srijan Das (University of North Carolina at Charlotte), Michael S Ryoo (Stony Brook/Google), Francois Bremond (Inria Sophia Antipolis, France)
The 34th British Machine Vision Conference
Abstract
The challenge of long-term video understanding remains constrained by the efficient extraction of object semantics and the modelling of their relationships for downstream tasks. Although OpenAI’s CLIP visual features exhibit discriminative properties for various vision tasks, particularly in object encoding, they are suboptimal for long-term video understanding. To address this issue, we present the Attributes-Aware Network (AAN), which consists of two key components: the Attributes Extractor and a Graph Reasoning block. These components facilitate the extraction of object-centric attributes and the modelling of their relationships within the video. By leveraging CLIP features, AAN outperforms state-of-the-art approaches on two popular action detection datasets: Charades and Toyota Smarthome Untrimmed datasets.Video
Citation
@inproceedings{Dai_2023_BMVC,
author = {Rui Dai and Srijan Das and Michael S Ryoo and Francois Bremond},
title = {Attributes-Aware Network for Temporal Action Detection},
booktitle = {34th British Machine Vision Conference 2023, {BMVC} 2023, Aberdeen, UK, November 20-24, 2023},
publisher = {BMVA},
year = {2023},
url = {https://papers.bmvc2023.org/0114.pdf}
}
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