[Paper Review] Video Summarization with Long Short-term Memory

Ke Zhang, Wei‐Lun Chao+2 more|arXiv (Cornell University)|May 26, 2016

Video Analysis and SummarizationComputer Science43 references81 citations

TL;DR

This paper proposes vsLSTM and dppLSTM, LSTM-based models that leverage sequential modeling and diversity regularization to generate high-quality video summaries by capturing long-range temporal dependencies and avoiding redundant frame selection. The approach achieves state-of-the-art performance on SumMe and TVSum benchmarks, especially when combining heterogeneous datasets via domain adaptation.

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.

Motivation & Objective

• To address the challenge of modeling variable-range temporal dependencies in video summarization, which are critical for capturing semantic storylines.
• To improve video summarization performance by leveraging supervised learning with human-annotated summaries.
• To reduce reliance on large-scale annotated data by adapting models across heterogeneous video datasets through domain adaptation.
• To enhance summary diversity and representativeness by integrating determinantal point processes (DPP) with LSTM.
• To demonstrate that sequential modeling via LSTMs significantly outperforms non-sequential baselines like multi-layer perceptrons (MLPs).

Proposed method

• The core model, vsLSTM, uses Long Short-Term Memory networks to encode sequential dependencies across video frames, learning to predict frame importance scores based on long-range context.
• The dppLSTM model combines LSTM with a Determinantal Point Process (DPP) to explicitly model inter-frame diversity, preventing selection of redundant, visually similar frames.
• The method treats video summarization as a structured prediction problem, where the output is a binary vector indicating selected frames or subshots.
• Domain adaptation is applied by linearly adapting visual features across datasets (e.g., SumMe and TVSum) to reduce statistical discrepancies between source and target domains.
• The model is trained end-to-end using supervised loss on human-annotated summaries, with optimization focusing on F1-score and recall.
• The approach uses frame-level features extracted from deep neural networks, with LSTM processing sequences to generate importance scores for each frame.

Experimental results

Research questions

• RQ1 Can LSTM-based models effectively model variable-range temporal dependencies in video summarization, leading to better performance than non-sequential models?
• RQ2 How can the diversity of selected frames/subshots be explicitly modeled to avoid redundancy in summaries?
• RQ3 Can domain adaptation techniques improve performance when training on limited annotated data by leveraging heterogeneous video datasets?
• RQ4 Does combining LSTM with DPP lead to more representative and diverse summaries compared to standalone LSTM or MLP baselines?
• RQ5 How does the model perform on real-world video datasets with varying visual styles and content complexity?

Key findings

• The dppLSTM model achieves state-of-the-art results on the TVSum dataset, with a 59.7% F1-score in the augmented setting using domain adaptation.
• On the SumMe dataset, dppLSTM reaches 44.7% F1-score in the augmented setting with domain adaptation, significantly outperforming baseline methods.
• The model with domain adaptation improves performance by up to 2.5% F1-score on SumMe and 1.8% on TVSum compared to training without adaptation.
• dppLSTM outperforms MLP-Shot in capturing temporally coherent, semantically important subshots, such as a dog’s ear cleaning sequence, which MLP-Shot fails to detect.
• Despite DPP’s tendency to avoid redundancy, dppLSTM successfully selects multiple important but visually similar subshots, demonstrating its ability to balance diversity and semantic relevance.
• The failure case in Fig. 5 shows that dppLSTM struggles with rapidly changing, densely clustered, and visually similar scenes, where recall drops due to over-penalization of redundancy.