Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation

• URL: http://arxiv.org/abs/2206.10095v1
• Date: Tue, 21 Jun 2022 03:40:58 GMT
• Title: Pyramid Region-based Slot Attention Network for Temporal Action Proposal Generation
• Authors: Shuaicheng Li, Feng Zhang, Rui-Wei Zhao, Rui Feng, Kunlin Yang, Lingbo Liu, Jun Hou
• Abstract summary: temporal action proposal generation can largely benefit from proper temporal and semantic context exploitation. We present a novel Pyramid Region-based Slot Attention Network PRSA-Net to learn a unified visual representation with rich temporal and semantic context.
• Score: 17.01865793062819
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: It has been found that temporal action proposal generation, which aims to discover the temporal action instances within the range of the start and end frames in the untrimmed videos, can largely benefit from proper temporal and semantic context exploitation. The latest efforts were dedicated to considering the temporal context and similarity-based semantic contexts through self-attention modules. However, they still suffer from cluttered background information and limited contextual feature learning. In this paper, we propose a novel Pyramid Region-based Slot Attention (PRSlot) module to address these issues. Instead of using the similarity computation, our PRSlot module directly learns the local relations in an encoder-decoder manner and generates the representation of a local region enhanced based on the attention over input features called \textit{slot}. Specifically, upon the input snippet-level features, PRSlot module takes the target snippet as \textit{query}, its surrounding region as \textit{key} and then generates slot representations for each \textit{query-key} slot by aggregating the local snippet context with a parallel pyramid strategy. Based on PRSlot modules, we present a novel Pyramid Region-based Slot Attention Network termed PRSA-Net to learn a unified visual representation with rich temporal and semantic context for better proposal generation. Extensive experiments are conducted on two widely adopted THUMOS14 and ActivityNet-1.3 benchmarks. Our PRSA-Net outperforms other state-of-the-art methods. In particular, we improve the AR@100 from the previous best 50.67% to 56.12% for proposal generation and raise the mAP under 0.5 tIoU from 51.9\% to 58.7\% for action detection on THUMOS14. \textit{Code is available at} \url{https://github.com/handhand123/PRSA-Net}

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