We introduce the novel problem of automatically generating animated GIFs from
video. GIFs are short looping video with no sound, and a perfect combination
between image and video that really capture our attention. GIFs tell a story,
express emotion, turn events into humorous moments, and are the new wave of
photojournalism. We pose the question: Can we automate the entirely manual and
elaborate process of GIF creation by leveraging the plethora of user generated
GIF content? We propose a Robust Deep RankNet that, given a video, generates a
ranked list of its segments according to their suitability as GIF. We train our
model to learn what visual content is often selected for GIFs by using over
100K user generated GIFs and their corresponding video sources. We effectively
deal with the noisy web data by proposing a novel adaptive Huber loss in the
ranking formulation. We show that our approach is robust to outliers and picks
up several patterns that are frequently present in popular animated GIFs. On
our new large-scale benchmark dataset, we show the advantage of our approach
over several state-of-the-art methods.

本文提出了一种利用用户生成的 GIF 内容自动生成 GIF 的方法，并提出了一种 Robust Deep RankNet，用于根据 suitability 生成 video 的 ranked list。在大量的用户生成数据上，本模型可识别出流行的 GIF 模式并具有鲁棒性（robust）和较高的准确度（advantage）。

从视频自动生成动态 GIF：Video2GIF

Video2GIF: Automatic Generation of Animated GIFs from Video

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.

本研究收集了 10 万个 GIF 图像，并基于众包技术获取了 120k 自然语言描述，以促进对动态图像的理解和自然语言描述生成技术的研究，该研究提供了一个理想的基准来评估视觉内容字幕的任务。研究还提供了基于最近邻算法、统计机器翻译和递归神经网络的动态 gif 描述任务的基线结果，并证明使用该数据集微调的模型对于自动电影描述是有帮助的。