Unsupervised Video Summarization
14 papers with code • 2 benchmarks • 3 datasets
Unsupervised video summarization approaches overcome the need for ground-truth data (whose production requires time-demanding and laborious manual annotation procedures), based on learning mechanisms that require only an adequately large collection of original videos for their training. Specifically, the training is based on heuristic rules, like the sparsity, the representativeness, and the diversity of the utilized input features/characteristics.
TVSum
SumMe
OSTD
Latest papers with no code
Unsupervised Video Summarization
This paper introduces a new, unsupervised method for automatic video summarization using ideas from generative adversarial networks but eliminating the discriminator, having a simple loss function, and separating training of different parts of the model.
Self-Attention Based Generative Adversarial Networks For Unsupervised Video Summarization
Experimental results indicate that using a self-attention mechanism as the frame selection mechanism outperforms the state-of-the-art on SumMe and leads to comparable to state-of-the-art performance on TVSum and COGNIMUSE.
Masked Autoencoder for Unsupervised Video Summarization
Summarizing a video requires a diverse understanding of the video, ranging from recognizing scenes to evaluating how much each frame is essential enough to be selected as a summary.
Learning to Summarize Videos by Contrasting Clips
Video summarization aims at choosing parts of a video that narrate a story as close as possible to the original one.
Unsupervised Video Summarization with a Convolutional Attentive Adversarial Network
Specifically, the generator employs a fully convolutional sequence network to extract global representation of a video, and an attention-based network to output normalized importance scores.
Cycle-SUM: Cycle-consistent Adversarial LSTM Networks for Unsupervised Video Summarization
The evaluator defines a learnable information preserving metric between original video and summary video and "supervises" the selector to identify the most informative frames to form the summary video.
FrameRank: A Text Processing Approach to Video Summarization
In constructing the dataset, because of the subjectivity of user-generated video summarization, we manually annotate 25 summaries for each video, which are in total 1300 summaries.
Unsupervised Object-Level Video Summarization with Online Motion Auto-Encoder
Unsupervised video summarization plays an important role on digesting, browsing, and searching the ever-growing videos every day, and the underlying fine-grained semantic and motion information (i. e., objects of interest and their key motions) in online videos has been barely touched.
TVSum: Summarizing Web Videos Using Titles
We observe that a video title is often carefully chosen to be maximally descriptive of its main topic, and hence images related to the title can serve as a proxy for important visual concepts of the main topic.
