Early Exit and Multi Stage Knowledge Distillation in VLMs for Video Summarization (2504.21831v1)

Abstract: We introduce DEEVISum (Distilled Early Exit Vision LLM for Summarization), a lightweight, efficient, and scalable vision LLM designed for segment wise video summarization. Leveraging multi modal prompts that combine textual and audio derived signals, DEEVISum incorporates Multi Stage Knowledge Distillation (MSKD) and Early Exit (EE) to strike a balance between performance and efficiency. MSKD offers a 1.33% absolute F1 improvement over baseline distillation (0.5%), while EE reduces inference time by approximately 21% with a 1.3 point drop in F1. Evaluated on the TVSum dataset, our best model PaLI Gemma2 3B + MSKD achieves an F1 score of 61.1, competing the performance of significantly larger models, all while maintaining a lower computational footprint. We publicly release our code and processed dataset to support further research.

• The paper introduces DEEVISum, a vision-language model for video summarization employing multi-stage knowledge distillation and early exit strategies to balance efficiency and performance.
• Multi-stage knowledge distillation achieved a 1.33% absolute F1 improvement over single-stage methods by transferring knowledge hierarchically between model sizes.
• An early exit strategy reduced inference time by approximately 21% with only a 1.3-point F1 score decrease, enabling faster processing at intermediate layers.

Analysis of DEEVISum: An Advanced VLM for Video Summarization

The paper focuses on the development and evaluation of DEEVISum, a vision-LLM that aims to efficiently summarize videos by capitalizing on multi-modal data inputs. The work explores how modern vision-LLMs (VLMs) can be effectively used in the task of video summarization, a domain challenged by the increasing demand for efficient computation and quick inference.

Core Contributions of DEEVISum

The paper introduces several methodological innovations designed to balance the trade-off between computation costs and model efficacy:

• Multi-Stage Knowledge Distillation (MSKD): The paper presents a hierarchical knowledge distillation approach where a series of models, decreasing in size from teacher to mentor and finally to student, are used to gradually transfer knowledge. This multi-tiered approach surpasses the traditional single-stage distillation methods, yielding a notable absolute improvement of 1.33% in the F1-score.
• Early Exit (EE) Strategy: The implementation of early exit mechanisms tackles the issue of long inference times in large models. By allowing the model to make predictions at intermediate layers, the authors demonstrate a reduction in inference time by approximately 21%, albeit with a minor trade-off of a 1.3-point decrease in F1-score.

Performance Evaluation

The model was tested on the TVSum dataset, where the optimal configuration of PaLI-Gemma2-3B combined with MSKD achieved a competitive F1 score of 61.1. It was validated against larger state-of-the-art models, indicating that DEEVISum can achieve similar levels of performance with significantly fewer computational resources.

Methodology

1. Knowledge Distillation: Distillation is conducted in stages, facilitating smoother transitions of knowledge and effectively bridging the representational capacity between larger teachers and more compact student models.
2. Early Exit Mechanism: Taking inspiration from existing early exit frameworks, DEEVISum dynamically chooses when to compute subsequent layers based on the confidence of intermediate predictions. This method strategically reduces facility usage and speeds up the analysis process, especially for simpler data cases.
3. Multimodal Prompt Engineering: By integrating not only visual but also textual data, such as transcripts, and audio cues like speaker identification and emotional expression, the model captures a broader semantic understanding, thus enhancing summarization accuracy.

Implications and Future Directions

In the field of VLM-based video summarization, DEEVISum represents a significant step forward by integrating advanced knowledge distillation and early-exit strategies to yield both efficiency and competitive performance. The success of this model opens several avenues for the future. Further refinement in early-exit strategies could minimize accuracy loss, and similar techniques could be adapted across other multi-modal learning tasks such as video captioning or content recommendation systems.

Additionally, the enriched data representation through multi-modal inputs suggests that a wider application and exploration of audio-visual data in AI tasks could lead to even more sophisticated understanding and manipulation of multimedia content.

DEEVISum signifies the practicality of efficient VLMs in real-time applications, thus contributing to scalable AI that is both environmentally sustainable and accessible across varying computational infrastructures. The methodology and results discussed set a foundational framework for future research in efficient video summarization, pushing the boundaries of current AI capabilities while simultaneously addressing existing limitations.