CLIP-It! Language-Guided Video Summarization (2107.00650v2)

Abstract: A generic video summary is an abridged version of a video that conveys the whole story and features the most important scenes. Yet the importance of scenes in a video is often subjective, and users should have the option of customizing the summary by using natural language to specify what is important to them. Further, existing models for fully automatic generic summarization have not exploited available LLMs, which can serve as an effective prior for saliency. This work introduces CLIP-It, a single framework for addressing both generic and query-focused video summarization, typically approached separately in the literature. We propose a language-guided multimodal transformer that learns to score frames in a video based on their importance relative to one another and their correlation with a user-defined query (for query-focused summarization) or an automatically generated dense video caption (for generic video summarization). Our model can be extended to the unsupervised setting by training without ground-truth supervision. We outperform baselines and prior work by a significant margin on both standard video summarization datasets (TVSum and SumMe) and a query-focused video summarization dataset (QFVS). Particularly, we achieve large improvements in the transfer setting, attesting to our method's strong generalization capabilities.

Overview of CLIP-It! Language-Guided Video Summarization

The paper introduces CLIP-It, a unified framework for video summarization that integrates language guidance into both generic and query-focused tasks. This approach leverages the advancements in multimodal transformers to address two traditionally disjoint tasks within video summarization literature. The model innovatively incorporates language guidance via pre-trained LLMs, which enables users to customize video summaries through natural language inputs—a notable step towards intuitive human-computer interaction.

Methodology and Results

CLIP-It utilizes a multimodal transformer to score frames based on their relevance and correlation to user-defined queries or auto-generated dense video captions. Key technical advancements include the Language-Guided Attention module, which facilitates efficient embedding fusion across video and language modalities, and the Frame-Scoring Transformer, which assigns scores by attending to contextual relationships. The model employs reconstruction and diversity losses to enable unsupervised training, thus broadening its applicability.

By innovating a language-guided approach to video summarization, CLIP-It achieves significant performance improvements across standard benchmarks, such as TVSum and SumMe, outperforming existing baselines. Notably, it demonstrates strong generalization capabilities by showing substantial gains in transfer settings, which corroborates the robustness and adaptability of integrating LLMs into video summarization tasks.

Implications for Research and Practice

The integration of LLMs as a conditioning factor in video summarization represents a significant stride in leveraging semantic information for enhanced interpretability and usability. This development opens pathways for implementing personalized video content consumption in practical applications, potentially benefiting platforms like YouTube that rely heavily on video metadata for user engagement.

From a theoretical standpoint, the paper challenges the conventional separation between visual and textual modalities, promoting a unified treatment in AI models that can yield more human-like understanding and interaction capabilities. Future research could explore deeper integrative models and expand into multilingual capacities, considering linguistic diversity in video contents.

Future Directions

Looking forward, this framework has potential for further exploration and refinement in several domains. Integrating real-time video content processing with language-based queries could revolutionize industries reliant on large-scale video data, such as surveillance, multimedia delivery, and virtual reality environments. Enhancements in the synthesis efficiency and accuracy of video-captioning models directly impact the quality of summarization outputs, advocating for continued innovation in transformer architectures and multimodal learning strategies.

In conclusion, CLIP-It significantly advances the field of video summarization, providing a crucial bridge between human language understanding and machine vision, and sets the stage for continued evolution in machine learning applications that seamlessly integrate multimodal data inputs.