Supervised Video Summarization
7 papers with code • 2 benchmarks • 3 datasets
Supervised video summarization rely on datasets with human-labeled ground-truth annotations (either in the form of video summaries, as in the case of the SumMe dataset, or in the form of frame-level importance scores, as in the case of the TVSum dataset), based on which they try to discover the underlying criterion for video frame/fragment selection and video summarization.
Source: Video Summarization Using Deep Neural Networks: A Survey
Benchmarks
These leaderboards are used to track progress in Supervised Video Summarization
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
TVSum
SumMe
Latest papers with no code
Relational Reasoning Over Spatial-Temporal Graphs for Video Summarization
Then, we construct a temporal graph by using the aggregated representations of spatial graphs.
A Stacking Ensemble Approach for Supervised Video Summarization
This paper investigates the underlying complementarity between the frame-level and shot-level methods, and a stacking ensemble approach is proposed for supervised video summarization.
Use of Affective Visual Information for Summarization of Human-Centric Videos
Then, we integrate the estimated emotional attributes and the high-level representations from the CER-NET with the visual information to define the proposed affective video summarization architectures (AVSUM).
How Good is a Video Summary? A New Benchmarking Dataset and Evaluation Framework Towards Realistic Video Summarization
For long videos, human reference summaries necessary for supervised video summarization techniques are difficult to obtain.
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning
For each subtask, the manager is trained to set a subgoal only by a task-level binary label, which requires much fewer labels than conventional approaches.
Weakly-supervised Video Summarization using Variational Encoder-Decoder and Web Prior
Video summarization is a challenging under-constrained problem because the underlying summary of a single video strongly depends on users' subjective understandings.
Improving Sequential Determinantal Point Processes for Supervised Video Summarization
In terms of modeling, we design a new probabilistic distribution such that, when it is integrated into SeqDPP, the resulting model accepts user input about the expected length of the summary.
How Local is the Local Diversity? Reinforcing Sequential Determinantal Point Processes with Dynamic Ground Sets for Supervised Video Summarization
The large volume of video content and high viewing frequency demand automatic video summarization algorithms, of which a key property is the capability of modeling diversity.
Video Summarization with Attention-Based Encoder-Decoder Networks
This paper addresses the problem of supervised video summarization by formulating it as a sequence-to-sequence learning problem, where the input is a sequence of original video frames, the output is a keyshot sequence.
Diverse Sequential Subset Selection for Supervised Video Summarization
Video summarization is a challenging problem with great application potential.
