# Relation-Aware Pyramid Network (RapNet) for temporal action proposal

## TL;DR

This paper introduces RapNet, a relation-aware pyramid network for generating temporal action proposals in videos, utilizing a fine-tuned CNN, multi-scale proposals, boundary adjustment, and ensemble methods, achieving second place in a challenge.

## Contribution

The paper presents a novel relation-aware pyramid network architecture for improved temporal action proposal generation in videos.

## Key findings

- Achieved 2nd place in ActivityNet Challenge 2019.
- Effective multi-scale proposal generation with boundary adjustment.
- Enhanced performance through ensemble methods.

## Abstract

In this technical report, we describe our solution to temporal action proposal (task 1) in ActivityNet Challenge 2019. First, we fine-tune a ResNet-50-C3D CNN on ActivityNet v1.3 based on Kinetics pretrained model to extract snippet-level video representations and then we design a Relation-Aware Pyramid Network (RapNet) to generate temporal multiscale proposals with confidence score. After that, we employ a two-stage snippet-level boundary adjustment scheme to re-rank the order of generated proposals. Ensemble methods are also been used to improve the performance of our solution, which helps us achieve 2nd place.