Video Summarization with Long Short-term Memory (1605.08110v2)

Abstract: We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the problem as a structured prediction problem on sequential data, our main idea is to use Long Short-Term Memory (LSTM), a special type of recurrent neural networks to model the variable-range dependencies entailed in the task of video summarization. Our learning models attain the state-of-the-art results on two benchmark video datasets. Detailed analysis justifies the design of the models. In particular, we show that it is crucial to take into consideration the sequential structures in videos and model them. Besides advances in modeling techniques, we introduce techniques to address the need of a large number of annotated data for training complex learning models. There, our main idea is to exploit the existence of auxiliary annotated video datasets, albeit heterogeneous in visual styles and contents. Specifically, we show domain adaptation techniques can improve summarization by reducing the discrepancies in statistical properties across those datasets.

• The paper presents a novel supervised LSTM model that captures both past and future frame dependencies to generate effective video summaries.
• It integrates determinantal point processes with LSTM to enhance diversity and reduce redundancy in keyframe selection.
• The study demonstrates robust improvements through domain adaptation and data augmentation on benchmark video datasets.

Video Summarization with Long Short-Term Memory

The paper "Video Summarization with Long Short-Term Memory" introduces a supervised learning approach for generating video summaries by selecting essential frames or subshots, using Long Short-Term Memory (LSTM) networks. The authors frame the problem as structured prediction, leveraging LSTMs to model temporal dependencies among video frames effectively. They demonstrate the model's capability to produce representative and compact video summaries, achieving state-of-the-art results on benchmark datasets.

Contributions

1. LSTM-based Model for Video Summarization: The paper proposes the \textsf{vsLSTM}, a model that utilizes bidirectional LSTM layers to account for both past and future dependencies in video sequences. The integration of sequential modeling is shown to be a critical aspect for effective summarization.
2. Combining LSTM with Determinantal Point Processes (DPPs): To enhance diversity in the selected keyframes, the research introduces \textsf{dppLSTM}, which combines LSTMs with DPPs. The architecture captures both frame-level importance and diversity, addressing possible redundancy in the summaries.
3. Domain Adaptation for Training Data: Recognizing the challenge of limited annotated video data, the authors propose using auxiliary datasets for training. They exploit domain adaptation techniques to mitigate discrepancies in data distribution across diverse video datasets, further improving the model's performance.

Results and Analysis

The paper reports that the proposed models outperform existing methods in various settings. In particular, they demonstrate:

• Superior Performance with Increased Data: By augmenting the training dataset with additional video summaries, the models achieve enhanced accuracy and can generalize better to unseen videos.
• Importance of Sequence Modeling: Comparisons with feedforward neural networks highlight LSTMs' advantage in capturing temporal dependencies vital for effective summarization.
• Effective Domain Adaptation: The authors show that even with disparate datasets, domain adaptation substantially improves performance, indicating the model's robustness in different scenarios.

Implications and Future Directions

The research opens multiple avenues in video summarization and structured predictions:

• Incorporating Semantics: Future work could focus on integrating high-level semantic understanding into video summarization, enhancing models to recognize and preserve crucial events better.
• Advanced Temporal Modeling: Further exploration into more sophisticated temporal architectures could allow even deeper insights into frame dependencies, potentially improving performance in scenarios with complex temporal structuring.
• Scalable Summarization: As video data continues to grow, developing models that can work efficiently on large-scale datasets remains crucial, especially those that can learn from weakly labeled or unlabeled datasets.

Conclusion

The paper makes a significant contribution to the video summarization field by effectively utilizing LSTMs for capturing essential temporal dependencies in video data. The approach demonstrates the practicality of combining sequential modeling with probabilistic methods like DPPs to enhance summarization quality. The strategies developed for using heterogeneous datasets and domain adaptation further underline this work's potential impact on future video content analysis tools.