# LTC-GIF: Attracting More Clicks on Feature-length Sports Videos

arXiv: https://arxiv.org/abs/2201.09077 · HTML: https://mlsanity.com/paper/2201.09077/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 42 references that are in the map.
- First posted on arXiv 22 Jan 2022.

## Builds on (most cited first, 17 of 17)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Two-Stream Convolutional Networks for Action Recognition in Videos](https://mlsanity.com/paper/1406.2199/) (2014)
- [Long-term Recurrent Convolutional Networks for Visual Recognition and Description](https://mlsanity.com/paper/1411.4389/) (2014)
- [MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation](https://mlsanity.com/paper/1903.01945/) (2019)
- [To Click or Not To Click: Automatic Selection of Beautiful Thumbnails from Videos](https://mlsanity.com/paper/1609.01388/) (2016)
- [Two-stream Collaborative Learning with Spatial-Temporal Attention for Video Classification](https://mlsanity.com/paper/1711.03273/) (2017)
- [ODN: Opening the Deep Network for Open-set Action Recognition](https://mlsanity.com/paper/1901.07757/) (2019)
- [Temporal Attention-Gated Model for Robust Sequence Classification](https://mlsanity.com/paper/1612.00385/) (2016)
- [Sentence Specified Dynamic Video Thumbnail Generation](https://mlsanity.com/paper/1908.04052/) (2019)
- [Vortex Pooling: Improving Context Representation in Semantic Segmentation](https://mlsanity.com/paper/1804.06242/) (2018)
- [Exploring Frame Segmentation Networks for Temporal Action Localization](https://mlsanity.com/paper/1902.05488/) (2019)
- [Deep Multi-Kernel Convolutional LSTM Networks and an Attention-Based Mechanism for Videos](https://mlsanity.com/paper/1908.08990/) (2019)

## Abstract (arXiv)

This paper proposes a lightweight method to attract users and increase views of the video by presenting personalized artistic media -- i.e, static thumbnails and animated GIFs. This method analyzes lightweight thumbnail containers (LTC) using computational resources of the client device to recognize personalized events from full-length sports videos. In addition, instead of processing the entire video, small video segments are processed to generate artistic media. This makes the proposed approach more computationally efficient compared to the baseline approaches that create artistic media using the entire video. The proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data used during artistic media generation. When extensive experiments were conducted on the Nvidia Jetson TX2, the computational complexity of the proposed method was 3.57 times lower than that of the SoA method. In the qualitative assessment, GIFs generated using the proposed method received 1.02 higher overall ratings compared to the SoA method. To the best of our knowledge, this is the first technique that uses LTC to generate artistic media while providing lightweight and high-performance services even on resource-constrained devices.