# Temporal Convolution Based Action Proposal: Submission to ActivityNet 2017

arXiv: https://arxiv.org/abs/1707.06750 · HTML: https://mlsanity.com/paper/1707.06750/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2017.

## Built on by (most cited first, 6 of 6)

- [Relaxed Transformer Decoders for Direct Action Proposal Generation](https://mlsanity.com/paper/2102.01894/) (2021)
- [Unsupervised Pre-training for Temporal Action Localization Tasks](https://mlsanity.com/paper/2203.13609/) (2022)
- [Agent-Environment Network for Temporal Action Proposal Generation](https://mlsanity.com/paper/2107.08323/) (2021)
- [ABN: Agent-Aware Boundary Networks for Temporal Action Proposal Generation](https://mlsanity.com/paper/2203.08942/) (2022)
- [SegTAD: Precise Temporal Action Detection via Semantic Segmentation](https://mlsanity.com/paper/2203.01542/) (2022)
- [BID: Boundary-Interior Decoding for Unsupervised Temporal Action Localization Pre-Trainin](https://mlsanity.com/paper/2403.07354/) (2024)

## Abstract (arXiv)

In this notebook paper, we describe our approach in the submission to the temporal action proposal (task 3) and temporal action localization (task 4) of ActivityNet Challenge hosted at CVPR 2017. Since the accuracy in action classification task is already very high (nearly 90% in ActivityNet dataset), we believe that the main bottleneck for temporal action localization is the quality of action proposals. Therefore, we mainly focus on the temporal action proposal task and propose a new proposal model based on temporal convolutional network. Our approach achieves the state-of-the-art performances on both temporal action proposal task and temporal action localization task.