# Dilated Temporal Relational Adversarial Network for Generic Video Summarization

arXiv: https://arxiv.org/abs/1804.11228 · HTML: https://mlsanity.com/paper/1804.11228/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2018.

## Built on by (most cited first, 11 of 11)

- [Video Summarization Using Deep Neural Networks: A Survey](https://mlsanity.com/paper/2101.06072/) (2021)
- [VideoXum: Cross-modal Visual and Textural Summarization of Videos](https://mlsanity.com/paper/2303.12060/) (2023)
- [GPT2MVS: Generative Pre-trained Transformer-2 for Multi-modal Video Summarization](https://mlsanity.com/paper/2104.12465/) (2021)
- [DeepQAMVS: Query-Aware Hierarchical Pointer Networks for Multi-Video Summarization](https://mlsanity.com/paper/2105.06441/) (2021)
- [Causalainer: Causal Explainer for Automatic Video Summarization](https://mlsanity.com/paper/2305.00455/) (2023)
- [Conditional Modeling Based Automatic Video Summarization](https://mlsanity.com/paper/2311.12159/) (2023)
- [Causal Video Summarizer for Video Exploration](https://mlsanity.com/paper/2307.01947/) (2023)
- [Enabling Harmonious Human-Machine Interaction with Visual-Context Augmented Dialogue System: A Review](https://mlsanity.com/paper/2207.00782/) (2022)
- ["Previously on ..." From Recaps to Story Summarization](https://mlsanity.com/paper/2405.11487/) (2024)
- [Visual Subtitle Feature Enhanced Video Outline Generation](https://mlsanity.com/paper/2208.11307/) (2022)
- [SELF-VS: Self-supervised Encoding Learning For Video Summarization](https://mlsanity.com/paper/2303.15993/) (2023)

## Abstract (arXiv)

The large amount of videos popping up every day, make it more and more critical that key information within videos can be extracted and understood in a very short time. Video summarization, the task of finding the smallest subset of frames, which still conveys the whole story of a given video, is thus of great significance to improve efficiency of video understanding. We propose a novel Dilated Temporal Relational Generative Adversarial Network (DTR-GAN) to achieve frame-level video summarization. Given a video, it selects the set of key frames, which contain the most meaningful and compact information. Specifically, DTR-GAN learns a dilated temporal relational generator and a discriminator with three-player loss in an adversarial manner. A new dilated temporal relation (DTR) unit is introduced to enhance temporal representation capturing. The generator uses this unit to effectively exploit global multi-scale temporal context to select key frames and to complement the commonly used Bi-LSTM. To ensure that summaries capture enough key video representation from a global perspective rather than a trivial randomly shorten sequence, we present a discriminator that learns to enforce both the information completeness and compactness of summaries via a three-player loss. The loss includes the generated summary loss, the random summary loss, and the real summary (ground-truth) loss, which play important roles for better regularizing the learned model to obtain useful summaries. Comprehensive experiments on three public datasets show the effectiveness of the proposed approach.