# Exploiting Context Information for Generic Event Boundary Captioning

arXiv: https://arxiv.org/abs/2207.01050 · HTML: https://mlsanity.com/paper/2207.01050/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 3 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Omnivore: A Single Model for Many Visual Modalities](https://mlsanity.com/paper/2201.08377/) (2022)
- [End-to-End Dense Video Captioning with Parallel Decoding](https://mlsanity.com/paper/2108.07781/) (2021)

## Built on by (most cited first, 3 of 3)

- [V2Xum-LLM: Cross-Modal Video Summarization with Temporal Prompt Instruction Tuning](https://mlsanity.com/paper/2404.12353/) (2024)
- [Empowering LLMs with Pseudo-Untrimmed Videos for Audio-Visual Temporal Understanding](https://mlsanity.com/paper/2403.16276/) (2024)
- [LLMVA-GEBC: Large Language Model with Video Adapter for Generic Event Boundary Captioning](https://mlsanity.com/paper/2306.10354/) (2023)

## Abstract (arXiv)

Generic Event Boundary Captioning (GEBC) aims to generate three sentences describing the status change for a given time boundary. Previous methods only process the information of a single boundary at a time, which lacks utilization of video context information. To tackle this issue, we design a model that directly takes the whole video as input and generates captions for all boundaries parallelly. The model could learn the context information for each time boundary by modeling the boundary-boundary interactions. Experiments demonstrate the effectiveness of context information. The proposed method achieved a 72.84 score on the test set, and we reached the 2nd place in this challenge. Our code is available at: urlhttps://github.com/zjr2000/Context-GEBC