# CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016

arXiv: https://arxiv.org/abs/1608.00797 · HTML: https://mlsanity.com/paper/1608.00797/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2016.

## Built on by (most cited first, 30 of 53)

- [ActionFormer: Localizing Moments of Actions with Transformers](https://mlsanity.com/paper/2202.07925/) (2022)
- [End-to-end Temporal Action Detection with Transformer](https://mlsanity.com/paper/2106.10271/) (2021)
- [VideoLLM: Modeling Video Sequence with Large Language Models](https://mlsanity.com/paper/2305.13292/) (2023)
- [Temporal Context Aggregation Network for Temporal Action Proposal Refinement](https://mlsanity.com/paper/2103.13141/) (2021)
- [TALLFormer: Temporal Action Localization with a Long-memory Transformer](https://mlsanity.com/paper/2204.01680/) (2022)
- [ReAct: Temporal Action Detection with Relational Queries](https://mlsanity.com/paper/2207.07097/) (2022)
- [End-to-End Temporal Action Detection with 1B Parameters Across 1000 Frames](https://mlsanity.com/paper/2311.17241/) (2023)
- [An Empirical Study of End-to-End Temporal Action Detection](https://mlsanity.com/paper/2204.02932/) (2022)
- [Self-Supervised Learning for Semi-Supervised Temporal Action Proposal](https://mlsanity.com/paper/2104.03214/) (2021)
- [Class Semantics-based Attention for Action Detection](https://mlsanity.com/paper/2109.02613/) (2021)
- [DCAN: Improving Temporal Action Detection via Dual Context Aggregation](https://mlsanity.com/paper/2112.03612/) (2021)
- [Deep Learning-based Action Detection in Untrimmed Videos: A Survey](https://mlsanity.com/paper/2110.00111/) (2021)
- [Graph Convolutional Module for Temporal Action Localization in Videos](https://mlsanity.com/paper/2112.00302/) (2021)
- [Action Sensitivity Learning for Temporal Action Localization](https://mlsanity.com/paper/2305.15701/) (2023)
- [Augmented Transformer with Adaptive Graph for Temporal Action Proposal Generation](https://mlsanity.com/paper/2103.16024/) (2021)
- [Self-Feedback DETR for Temporal Action Detection](https://mlsanity.com/paper/2308.10570/) (2023)
- [Hear Me Out: Fusional Approaches for Audio Augmented Temporal Action Localization](https://mlsanity.com/paper/2106.14118/) (2021)
- [Colar: Effective and Efficient Online Action Detection by Consulting Exemplars](https://mlsanity.com/paper/2203.01057/) (2022)
- [Low-Fidelity End-to-End Video Encoder Pre-training for Temporal Action Localization](https://mlsanity.com/paper/2103.15233/) (2021)
- [Learning to Refactor Action and Co-occurrence Features for Temporal Action Localization](https://mlsanity.com/paper/2206.11493/) (2022)
- [Temporal Action Proposal Generation with Transformers](https://mlsanity.com/paper/2105.12043/) (2021)
- [Adapting Short-Term Transformers for Action Detection in Untrimmed Videos](https://mlsanity.com/paper/2312.01897/) (2023)
- [Deep Learning for Video-Text Retrieval: a Review](https://mlsanity.com/paper/2302.12552/) (2023)
- [AOE-Net: Entities Interactions Modeling with Adaptive Attention Mechanism for Temporal Action Proposals Generation](https://mlsanity.com/paper/2210.02578/) (2022)
- [Temporally smooth online action detection using cycle-consistent future anticipation](https://mlsanity.com/paper/2104.08030/) (2021)
- [Winning the CVPR'2021 Kinetics-GEBD Challenge: Contrastive Learning Approach](https://mlsanity.com/paper/2106.11549/) (2021)
- [Relational Graph Learning for Grounded Video Description Generation](https://mlsanity.com/paper/2112.00967/) (2021)
- [AEI: Actors-Environment Interaction with Adaptive Attention for Temporal Action Proposals Generation](https://mlsanity.com/paper/2110.11474/) (2021)
- [A Survey on Deep Learning Techniques for Action Anticipation](https://mlsanity.com/paper/2309.17257/) (2023)
- [Faster-TAD: Towards Temporal Action Detection with Proposal Generation and Classification in a Unified Network](https://mlsanity.com/paper/2204.02674/) (2022)

## Abstract (arXiv)

This paper presents the method that underlies our submission to the untrimmed video classification task of ActivityNet Challenge 2016. We follow the basic pipeline of temporal segment networks and further raise the performance via a number of other techniques. Specifically, we use the latest deep model architecture, e.g., ResNet and Inception V3, and introduce new aggregation schemes (top-k and attention-weighted pooling). Additionally, we incorporate the audio as a complementary channel, extracting relevant information via a CNN applied to the spectrograms. With these techniques, we derive an ensemble of deep models, which, together, attains a high classification accuracy (mAP 93.23%) on the testing set and secured the first place in the challenge.