# The ActivityNet Large-Scale Activity Recognition Challenge 2018 Summary

arXiv: https://arxiv.org/abs/1808.03766 · HTML: https://mlsanity.com/paper/1808.03766/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2018.

## Built on by (most cited first, 15 of 15)

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- [ForgeryNet: A Versatile Benchmark for Comprehensive Forgery Analysis](https://mlsanity.com/paper/2103.05630/) (2021)
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- [Stand-Alone Inter-Frame Attention in Video Models](https://mlsanity.com/paper/2206.06931/) (2022)
- [Look, Listen, and Attack: Backdoor Attacks Against Video Action Recognition](https://mlsanity.com/paper/2301.00986/) (2023)
- [Bi-Calibration Networks for Weakly-Supervised Video Representation Learning](https://mlsanity.com/paper/2206.10491/) (2022)
- [BBA: Bi-Modal Behavioral Alignment for Reasoning with Large Vision-Language Models](https://mlsanity.com/paper/2402.13577/) (2024)
- [Projection: A Mechanism for Human-like Reasoning in Artificial Intelligence](https://mlsanity.com/paper/2103.13512/) (2021)
- [ForgeryNet -- Face Forgery Analysis Challenge 2021: Methods and Results](https://mlsanity.com/paper/2112.08325/) (2021)
- [Live Video Captioning](https://mlsanity.com/paper/2406.14206/) (2024)
- [Do sound event representations generalize to other audio tasks? A case study in audio transfer learning](https://mlsanity.com/paper/2106.11335/) (2021)
- [A Survey on Human Action Recognition](https://mlsanity.com/paper/2301.06082/) (2022)
- [HAVANA: Hierarchical stochastic neighbor embedding for Accelerated Video ANnotAtions](https://mlsanity.com/paper/2409.10641/) (2024)
- [Intention Driven Identification of In-Possession Match Phases in Association Football through Temporal Graph Learning](https://mlsanity.com/paper/2606.09289/) (2026)

## Abstract (arXiv)

The 3rd annual installment of the ActivityNet Large- Scale Activity Recognition Challenge, held as a full-day workshop in CVPR 2018, focused on the recognition of daily life, high-level, goal-oriented activities from user-generated videos as those found in internet video portals. The 2018 challenge hosted six diverse tasks which aimed to push the limits of semantic visual understanding of videos as well as bridge visual content with human captions. Three out of the six tasks were based on the ActivityNet dataset, which was introduced in CVPR 2015 and organized hierarchically in a semantic taxonomy. These tasks focused on tracing evidence of activities in time in the form of proposals, class labels, and captions. In this installment of the challenge, we hosted three guest tasks to enrich the understanding of visual information in videos. The guest tasks focused on complementary aspects of the activity recognition problem at large scale and involved three challenging and recently compiled datasets: the Kinetics-600 dataset from Google DeepMind, the AVA dataset from Berkeley and Google, and the Moments in Time dataset from MIT and IBM Research.