Video moment retrieval (MR) and highlight detection (HD) based on natural
language queries are two highly related tasks, which aim to obtain relevant
moments within videos and highlight scores of each video clip. Recently,
several methods have been devoted to building DETR-based networks to solve both
MR and HD jointly. These methods simply add two separate task heads after
multi-modal feature extraction and feature interaction, achieving good
performance. Nevertheless, these approaches underutilize the reciprocal
relationship between two tasks. In this paper, we propose a task-reciprocal
transformer based on DETR (TR-DETR) that focuses on exploring the inherent
reciprocity between MR and HD. Specifically, a local-global multi-modal
alignment module is first built to align features from diverse modalities into
a shared latent space. Subsequently, a visual feature refinement is designed to
eliminate query-irrelevant information from visual features for modal
interaction. Finally, a task cooperation module is constructed to refine the
retrieval pipeline and the highlight score prediction process by utilizing the
reciprocity between MR and HD. Comprehensive experiments on QVHighlights,
Charades-STA and TVSum datasets demonstrate that TR-DETR outperforms existing
state-of-the-art methods. Codes are available at
url{https://github.com/mingyao1120/TR-DETR}.
Video moment retrieval (MR) and highlight detection (HD) are two important tasks in the field of multimedia information systems. MR aims to find relevant moments within videos based on natural language queries, while HD focuses on determining the highlight scores of different video clips. Both tasks require the understanding and analysis of video content.
In recent years, researchers have been working on developing DETR-based networks to solve MR and HD jointly. However, these methods often treat the tasks as separate entities and fail to fully exploit the reciprocal relationship between them.
In this paper, the authors propose a task-reciprocal transformer based on DETR (TR-DETR) to leverage the inherent reciprocity between MR and HD. The TR-DETR model consists of several key components:
- Local-global multi-modal alignment module: This module aligns features from various modalities, such as text and video, into a shared latent space. By doing so, the model ensures that the features are well-integrated and can be effectively utilized for both MR and HD.
- Visual feature refinement: This module aims to eliminate query-irrelevant information from visual features, ensuring that the modal interaction is more focused and accurate. By refining the visual features, the model can better capture the relevant information for both tasks.
- Task cooperation module: This module is designed to refine the retrieval pipeline and the highlight score prediction process by utilizing the reciprocity between MR and HD. It allows the two tasks to mutually benefit from each other’s insights and improve overall performance.
The experiments conducted on QVHighlights, Charades-STA, and TVSum datasets showcase the superior performance of TR-DETR compared to existing state-of-the-art methods. The proposed model effectively leverages the reciprocal relationship between MR and HD, leading to more accurate and informative results.
The concepts discussed in this article have a multidisciplinary nature, combining elements from computer science, artificial intelligence, natural language processing, and multimedia systems. The development of advanced algorithms and models for MR and HD has implications for various applications, including content recommendation systems, video summarization, and interactive multimedia experiences.
Furthermore, the ideas presented here are closely related to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The ability to accurately retrieve video moments and detect highlights is crucial in creating immersive multimedia experiences and virtual environments. These technologies rely on the analysis and understanding of video content, which can benefit greatly from the advancements in MR and HD.
In conclusion, the task-reciprocal transformer based on DETR (TR-DETR) introduced in this paper demonstrates a novel approach to jointly solve video moment retrieval and highlight detection. By leveraging the reciprocal relationship between the two tasks, TR-DETR achieves superior performance compared to existing methods. The concepts discussed here have implications for multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, further advancing the field and enhancing user experiences.