Efficient Joint Moment Retrieval and Highlight Detection in Videos through Task-Driven Exploration

Temel Kavramlar

A novel task-driven, top-down framework for jointly addressing moment retrieval and highlight detection in videos, which captures task-specific and common representations, investigates the interplay between the two tasks, and utilizes a principled task-dependent joint loss function.

Özet

The paper proposes a novel task-driven, top-down framework called TaskWeave for jointly addressing moment retrieval (MR) and highlight detection (HD) in videos. The key ideas are:

Task-decoupled unit: Captures task-specific and common representations by using a shared expert and two task-specific experts.
Inter-task feedback mechanism: Transforms the results of one task (MR or HD) into guiding masks to assist the other task.
Task-dependent joint loss: Introduces a principled joint loss function where the task-specific weights are dynamically adjusted, rather than manually tuned.

The authors conduct extensive experiments on three benchmark datasets - QVHighlights, TVSum, and Charades-STA. The results show that TaskWeave outperforms existing state-of-the-art methods in both joint MR and HD, as well as individual MR and HD tasks. Ablation studies validate the effectiveness of the proposed components.

İstatistikler

The QVHighlights dataset provides 10,310 queries associated with 18,367 moments, with an average of 1.8 disjoint moments per query.
The Charades-STA dataset contains 16,128 query-moment pairs.
The TVSum dataset comprises videos from 10 domains, with each domain containing 5 videos.

Alıntılar

"Although existing studies have made impressive advancement recently, they predominantly follow the data-driven bottom-up paradigm. Such paradigm overlooks task-specific and inter-task effects, resulting in poor model performance."
"To this end, we propose a novel paradigm TaskWeave from a task-driven perspective. The key idea is to jointly address the tasks MR and HD by considering the commonality, specificity, and interplay of MR and HD."

Önemli Bilgiler Şuradan Elde Edildi

Task-Driven Exploration: Decoupling and Inter-Task Feedback for Joint Moment Retrieval and Highlight Detection

by Jin Yang,Pin... : arxiv.org 04-16-2024

https://arxiv.org/pdf/2404.09263.pdf

Daha Derin Sorular

How can the proposed task-driven framework be extended to other multi-task learning problems beyond moment retrieval and highlight detection

The proposed task-driven framework can be extended to other multi-task learning problems by adapting the key components and principles to suit the specific requirements of different tasks. Here are some ways to extend the framework:

Task-Decoupled Unit: The task-decoupled unit can be customized to capture task-specific and common features for new tasks. Depending on the nature of the tasks, different types of experts and network architectures can be incorporated into the framework to extract relevant features.

Inter-Task Feedback Mechanism: The inter-task feedback mechanism can be modified to suit the interplay between tasks in other domains. For example, in tasks where one task's output can provide valuable guidance for another task, similar feedback mechanisms can be implemented to enhance performance.

Task-Dependent Joint Loss: The task-dependent joint loss function can be adapted to different tasks by adjusting the weights and loss components based on the specific requirements of each task. This customization ensures that the model optimizes performance effectively for each task.

By customizing these components based on the characteristics of new multi-task learning problems, the task-driven framework can be successfully extended to a wide range of tasks beyond moment retrieval and highlight detection.

What are the potential limitations or challenges in applying the inter-task feedback mechanism, and how can they be addressed

One potential limitation in applying the inter-task feedback mechanism is the complexity of determining the optimal feedback strategy for different tasks. Addressing this challenge involves:

Task-Specific Feedback Strategies: Tailoring the feedback mechanisms to the specific requirements of each task can help overcome limitations. By understanding the interplay between tasks and the impact of feedback, task-specific strategies can be developed to optimize performance.

Dynamic Feedback Adjustment: Implementing dynamic adjustments in the feedback mechanism based on the model's learning progress can enhance adaptability. By monitoring the model's performance and adjusting the feedback strategy accordingly, the limitations of fixed feedback approaches can be mitigated.

Experimental Validation: Conducting thorough experiments and ablation studies to evaluate the effectiveness of different feedback mechanisms can provide insights into the most suitable strategies for specific tasks. This empirical validation helps in identifying and addressing potential limitations in the feedback process.

By incorporating these strategies, the challenges in applying the inter-task feedback mechanism can be addressed effectively.

How can the task-dependent joint loss be further improved to better balance the performance of different tasks

To further improve the task-dependent joint loss for better balancing the performance of different tasks, the following strategies can be considered:

Adaptive Weighting: Implementing adaptive weighting mechanisms that dynamically adjust the weights based on the model's learning progress can help in balancing the performance of different tasks. By continuously monitoring the task-specific losses and adjusting the weights accordingly, the model can optimize performance effectively.

Loss Function Refinement: Refining the components of the joint loss function based on the specific characteristics of each task can enhance the balance between tasks. By fine-tuning the loss components to better reflect the importance of different tasks, the model can achieve a more balanced optimization process.

Regularization Techniques: Incorporating regularization techniques such as L1 or L2 regularization in the joint loss function can help prevent overfitting and improve the generalization of the model. By adding regularization terms to the loss function, the model's performance on different tasks can be stabilized and balanced.

By implementing these strategies, the task-dependent joint loss can be further improved to achieve a better balance in optimizing the performance of different tasks within the multi-task learning framework.