# Past and Future Motion Guided Network for Audio Visual Event Localization

arXiv: https://arxiv.org/abs/2205.03802 · HTML: https://mlsanity.com/paper/2205.03802/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 8 May 2022.

## Builds on (most cited first, 15 of 15)

- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Self-Supervised Learning of Audio-Visual Objects from Video](https://mlsanity.com/paper/2008.04237/) (2020)
- [TEA: Temporal Excitation and Aggregation for Action Recognition](https://mlsanity.com/paper/2004.01398/) (2020)
- [Listen to Look: Action Recognition by Previewing Audio](https://mlsanity.com/paper/1912.04487/) (2019)
- [Music Gesture for Visual Sound Separation](https://mlsanity.com/paper/2004.09476/) (2020)
- [Optical Flow Guided Feature: A Fast and Robust Motion Representation for Video Action Recognition](https://mlsanity.com/paper/1711.11152/) (2017)
- [Dual-modality seq2seq network for audio-visual event localization](https://mlsanity.com/paper/1902.07473/) (2019)
- [MotionSqueeze: Neural Motion Feature Learning for Video Understanding](https://mlsanity.com/paper/2007.09933/) (2020)
- [Positive Sample Propagation along the Audio-Visual Event Line](https://mlsanity.com/paper/2104.00239/) (2021)
- [Look, Listen, and Attend: Co-Attention Network for Self-Supervised Audio-Visual Representation Learning](https://mlsanity.com/paper/2008.05789/) (2020)
- [Audio-Visual Event Localization via Recursive Fusion by Joint Co-Attention](https://mlsanity.com/paper/2008.06581/) (2020)
- [An Improved Event-Independent Network for Polyphonic Sound Event Localization and Detection](https://mlsanity.com/paper/2010.13092/) (2020)
- [Sound Event Detection Using Spatial Features and Convolutional Recurrent Neural Network](https://mlsanity.com/paper/1706.02291/) (2017)
- [Audio-Visual Event Recognition through the lens of Adversary](https://mlsanity.com/paper/2011.07430/) (2020)
- [Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition](https://mlsanity.com/paper/1912.00308/) (2019)

## Abstract (arXiv)

In recent years, audio-visual event localization has attracted much attention. It's purpose is to detect the segment containing audio-visual events and recognize the event category from untrimmed videos. Existing methods use audio-guided visual attention to lead the model pay attention to the spatial area of the ongoing event, devoting to the correlation between audio and visual information but ignoring the correlation between audio and spatial motion. We propose a past and future motion extraction (pf-ME) module to mine the visual motion from videos ,embedded into the past and future motion guided network (PFAGN), and motion guided audio attention (MGAA) module to achieve focusing on the information related to interesting events in audio modality through the past and future visual motion. We choose AVE as the experimental verification dataset and the experiments show that our method outperforms the state-of-the-arts in both supervised and weakly-supervised settings.