Video summarization
ACGAN[26]
46.0
58.5
SUM-GAN-AAE[27]
48.9
58.3
CRSUM[13]
47.3
58.0
DR-DSN [28]
41.4
57.6
EDVS [30]
42.6
57.3
The best-performing methods on both benchmark datasets are those based on Generative Adversarial Networks (GANs).
4 Conclusions Video summarization technology, as a crucial approach to managing the explosive growth of video data, has undergone a significant evolution from traditional handcrafted visual feature-based methods to automated intelligent techniques powered by deep learning. This paper systematically reviews the mainstream technologies in the field, including supervised learning approaches based on LSTM and Transformer models, unsupervised methods such as clustering and generative adversarial networks, and reinforcement learning frameworks, with a particular emphasis on the role of multimodal fusion in enhancing summary quality. Moreover, sports video summarization, as a representative application scenario, has driven advancements in action recognition and event localization. Despite considerable progress, several challenges remain in video summarization. First, the high cost of annotated data limits the general applicability of supervised methods, making the design of efficient weakly supervised or unsupervised learning strategies a critical issue. Second, the temporal continuity and semantic coherence of videos have yet to be fully leveraged, leaving room for improving the narrative and viewing experience of summaries. While multimodal information fusion significantly boosts model performance, effective collaboration among different modalities and noise handling still requires breakthroughs. Finally, with the increasing demand for real-time summarization, enhancing computational efficiency and deployability without compromising accuracy remains an important direction. Future research can be pursued in several directions: (1) exploring transfer learning across domains and tasks to alleviate dependence on large-scale annotated datasets; (2) integrating cutting-edge techniques such as graph neural networks and causal reasoning to deepen the understanding of temporal relationships and event causality; (3) advancing multimodal fusion strategies by combining visual, auditory, textual, and behavioral data to construct semantically richer summary models; and (4) promoting lightweight model design and hardware acceleration to achieve real-time and scalable video summarization.
105
Made with FlippingBook - PDF hosting