TGIF: A New Dataset and Benchmark on Animated GIF Description
This provides a testbed for animated GIF understanding, addressing a need in social media, but it is incremental as it builds on existing image and video description datasets.
The authors tackled the problem of indexing animated GIFs with rich metadata by creating the TGIF dataset, containing 100K GIFs and 120K descriptions, and showed it serves as a benchmark for visual content captioning with baseline results using techniques like recurrent neural networks.
With the recent popularity of animated GIFs on social media, there is need for ways to index them with rich metadata. To advance research on animated GIF understanding, we collected a new dataset, Tumblr GIF (TGIF), with 100K animated GIFs from Tumblr and 120K natural language descriptions obtained via crowdsourcing. The motivation for this work is to develop a testbed for image sequence description systems, where the task is to generate natural language descriptions for animated GIFs or video clips. To ensure a high quality dataset, we developed a series of novel quality controls to validate free-form text input from crowdworkers. We show that there is unambiguous association between visual content and natural language descriptions in our dataset, making it an ideal benchmark for the visual content captioning task. We perform extensive statistical analyses to compare our dataset to existing image and video description datasets. Next, we provide baseline results on the animated GIF description task, using three representative techniques: nearest neighbor, statistical machine translation, and recurrent neural networks. Finally, we show that models fine-tuned from our animated GIF description dataset can be helpful for automatic movie description.