Source-linked AI summary
Video Summarization Using Fully Convolutional Sequence Networks
Mrigank Rochan, Linwei Ye, Yang Wang
TL;DR
Video summarization seeks shorter videos that retain important information amid rapidly growing video data. This paper formulates selection as binary sequence labeling, connects it to semantic segmentation, and adapts fully convolutional models into FCSN. Across two benchmark datasets, the models achieve competitive or state-of-the-art performance relative to approaches mainly using LSTMs.
Problem
Video summarization must select informative frames from increasingly large video collections, while existing state-of-the-art approaches mainly use recurrent sequence models.
Method
The paper formulates summarization as binary frame-level sequence labeling and adapts semantic-segmentation architectures into fully convolutional sequence networks.
Results
The proposed models achieve state-of-the-art or very competitive performance on two benchmark datasets compared with supervised and unsupervised approaches mainly using LSTMs.
Takeaways & Limitations
Fully convolutional models provide a promising alternative to LSTM-based approaches for video summarization.
Takeaways & Limitations
The approach focuses on learning video summarization from binary-label annotations, although frame-level scores provide richer information.
Abstract
from arXiv · showhide
This paper addresses the problem of video summarization. Given an input video, the goal is to select a subset of the frames to create a summary video that optimally captures the important information of the input video. With the large amount of videos available online, video summarization provides a useful tool that assists video search, retrieval, browsing, etc. In this paper, we formulate video summarization as a sequence labeling problem. Unlike existing approaches that use recurrent models, we propose fully convolutional sequence models to solve video summarization. We firstly establish a novel connection between semantic segmentation and video summarization, and then adapt popular semantic segmentation networks for video summarization. Extensive experiments and analysis on two benchmark datasets demonstrate the effectiveness of our models.
1 Introduction
Video summarization selects frames that preserve important information, a task formulated here as binary sequence labeling. The paper connects this formulation to semantic segmentation and introduces fully convolutional sequence networks that process frames in parallel.
- Motivation: Video summarization selects a subset of frames to create a shorter video containing important information.Short summaries can reduce the effort of browsing long surveillance videos and support downstream video analysis.
- Problem Formulation: Each video frame receives a binary label indicating whether it is selected for the summary.The summary is formed by combining frames labeled 1.
- Motivation: Existing LSTM-based methods capture long-term frame dependencies but process frames sequentially, limiting straightforward GPU parallelization.The paper proposes fully convolutional models to process all frames simultaneously.
- Connection to Semantic Segmentation: Video summarization and semantic segmentation both assign labels across an ordered input, enabling semantic-segmentation architectures to be adapted to frame selection.Frames form a 1D temporal input with K channels, while the output has one selection indicator per frame.
- Proposed Approach: The Fully Convolutional Sequence Network adapts semantic-segmentation models using temporal convolutions and growing effective context to model dependencies among frames.Its fully convolutional structure also supports easier parallelization than LSTM.
- Contributions: Experiments on two benchmark datasets show that the proposed models achieve state-of-the-art performance.The paper reports both supervised and unsupervised fully convolutional models.
2 Related Work
Video summarization research spans multiple output formats and has progressed from heuristic selection toward supervised and deep recurrent approaches. Recent LSTM methods model long-range dependencies, diversity, reconstruction, and keyframe regularization.
- Summary Representations: Storyboard-based summarization produces either isolated keyframes or correlated consecutive keyshots.Keyshots treat a continuous temporal interval as the summarized unit.
- Early Methods: Early video-summarization methods mainly use hand-crafted heuristics to estimate frame importance or representativeness.Most of these approaches are unsupervised.
- Supervised Learning: Recent supervised approaches learn from videos paired with ground-truth annotations for summary selection.The supplied passage notes that annotation formats include frame-level scores and binary labels.
- Deep Learning Methods: LSTM-based methods model long-range dependencies among frames, using bidirectional sequence models or recurrent generative adversarial frameworks.One approach combines two LSTMs with a determinantal point process, while another uses an LSTM variational autoencoder and discriminator.
3 Our Approach
The approach formulates video summarization as frame-wise binary labeling and adapts fully convolutional semantic-segmentation models to temporal sequences. It includes supervised and unsupervised variants, with the latter encouraging visual diversity without ground-truth summaries.
- 3.1 Problem Formulation: Video summarization selects a subset of frames by assigning each frame a binary label and combining frames labeled 1.
- 3.2 Fully Convolutional Sequence Networks: FCSN replaces spatial semantic-segmentation operations with temporal convolutions, pooling, and deconvolutions, processing all frames simultaneously.
- 3.2 Fully Convolutional Sequence Networks: The encoder extracts high-level semantic and long-term structural information, while the decoder produces the sequence of 0/1 frame labels.
- 3.2 Fully Convolutional Sequence Networks: SUM-FCN adapts the FCN-16 semantic-segmentation architecture by converting its spatial convolutions, pooling, and deconvolutions into temporal counterparts.
- 3.2 Fully Convolutional Sequence Networks: Supervised learning uses a weighted loss to address the severe imbalance between selected keyframes and non-keyframes.
- 3.3 Unsupervised SUM-FCN: SUM-FCNunsup learns without ground-truth summaries by combining diversity and reconstruction losses for selected keyframes.
4 Experiments
Experiments on SumMe and TVSum evaluate SUM-FCN and related fully convolutional models under supervised, unsupervised, transfer, and longer-video settings. The models achieve strong benchmark performance, while analyses examine annotation formats, architectural variants, and video length.
- Datasets and setup: Experiments use the SumMe and TVSum benchmark datasets, supplemented by YouTube and OVP videos for training.SumMe contains 25 videos and TVSum contains 50 videos; YouTube and OVP are used only to augment training data.
- Datasets and setup: Training uses keyframe-based ground-truth annotations, while evaluation converts predictions and annotations to comparable keyshot summaries when required.TVSum frame-level importance scores are converted to keyshots for evaluation, whereas SumMe provides keyshot annotations directly.
- Main results: SUM-FCN outperforms other state-of-the-art methods on SumMe by a large margin and achieves state-of-the-art performance in the standard supervised TVSum setting.On TVSum, performance is comparable to other state-of-the-art methods in augmented and transfer settings despite using only keyframe-based annotations.
- Unsupervised analysis: SUM-FCNunsup achieves state-of-the-art performance on both datasets, showing that the fully convolutional sequence model can learn video summaries without supervision.The paper notes that this is appealing because collecting labeled training data is difficult.
- Architectural analysis: SUM-DeepLab performs better than SUM-FCN on SumMe in all settings and on TVSum in the standard supervised setting, with comparable performance in the other settings.Replacing bilinear upsampling with learnable deconvolution improves SUM-DeepLab in TVSum transfer evaluation, where it reaches state-of-the-art performance.
- Video-length analysis: With T=640 frames, performance is similar to T=320, and unsampled variable-length videos achieve comparable performance, indicating robustness to video length.The unsampled transfer result is even higher on SumMe than the fixed-length result.
5 Conclusion
The paper introduces fully convolutional sequence networks for video summarization by adapting semantic segmentation ideas. The models achieve competitive performance against supervised and unsupervised state-of-the-art approaches, while the framework can extend beyond the presented variants.
- FCSN models adapt fully convolutional semantic-segmentation networks to video summarization.The paper frames the two problems as sharing an underlying connection.
- The proposed models achieve very competitive performance compared with supervised and unsupervised state-of-the-art approaches that mainly use LSTMs.
- Similar strategies can convert almost any semantic segmentation network into a video-summarization counterpart.The authors identify exploration of newer semantic segmentation models as future work.