Enhancing Video Summarization with Context Awareness

TL;DR

The work addresses the challenge of efficiently summarizing abundant video content in the presence of limited labeled data and evaluation metrics that fail to capture temporal and semantic nuances. It introduces a training-free, unsupervised framework that uses pre-trained visual embeddings to generate context-aware summaries via a global-to-local semantic partitioning pipeline, combining dimensionality reduction (PCA, tsne), clustering (Birch, Agglomerative), and a knapsack-like mechanism to assemble compact summaries with keyframes and surrounding frames. A novel human-centric evaluation pipeline is proposed to assess informativeness and comprehension, including a structured video set, questionnaire types, and implementation via a web interface. Experimental results on SumMe demonstrate that the proposed method outperforms existing unsupervised approaches and is competitive with state-of-the-art supervised methods, while the ablation and qualitative analyses validate the importance of temporal context and partitioning. The findings highlight the potential of context-aware, training-free summarization to provide scalable, interpretable, and effective video summaries, with practical impact for large-scale video indexing and retrieval.

Abstract

Video summarization is a crucial research area that aims to efficiently browse and retrieve relevant information from the vast amount of video content available today. With the exponential growth of multimedia data, the ability to extract meaningful representations from videos has become essential. Video summarization techniques automatically generate concise summaries by selecting keyframes, shots, or segments that capture the video's essence. This process improves the efficiency and accuracy of various applications, including video surveillance, education, entertainment, and social media. Despite the importance of video summarization, there is a lack of diverse and representative datasets, hindering comprehensive evaluation and benchmarking of algorithms. Existing evaluation metrics also fail to fully capture the complexities of video summarization, limiting accurate algorithm assessment and hindering the field's progress. To overcome data scarcity challenges and improve evaluation, we propose an unsupervised approach that leverages video data structure and information for generating informative summaries. By moving away from fixed annotations, our framework can produce representative summaries effectively. Moreover, we introduce an innovative evaluation pipeline tailored specifically for video summarization. Human participants are involved in the evaluation, comparing our generated summaries to ground truth summaries and assessing their informativeness. This human-centric approach provides valuable insights into the effectiveness of our proposed techniques. Experimental results demonstrate that our training-free framework outperforms existing unsupervised approaches and achieves competitive results compared to state-of-the-art supervised methods.

Figures (24)

• Figure 1: High-level representation of the analysis pipeline of supervised algorithms that perform summarization by learning the frames' importance after modeling their temporal or spatiotemporal dependency. For the latter class of methods (i.e., modeling the spatiotemporal dependency among frames), object bounding boxes and object relations in time shown with dashed rectangles and lines, are used to illustrate the extension that models both the temporal and spatial dependency among frames.
• Figure 2: High-level representation of the analysis pipeline of supervised algorithms that learn summarization with the help of ground-truth data and adversarial learning.
• Figure 3: High-level representation of the analysis pipeline of unsupervised algorithms that learn summarization by increasing the similarity between the summary and the video.
• Figure 4: High-level representation of the analysis pipeline of supervised algorithms that learn summarization based on hand-crafted rewards and reinforcement learning.
• Figure 5: Pipeline of the proposed approach showcasing four modules and information flow across main stages. Contextual embeddings extracted from original video frames in the first stage and output summary generated in the final stage. Other information utilized in subsequent stages.