arXiv:2504.04840v1 Announce Type: new
Abstract: Even from an early age, humans naturally adapt between exocentric (Exo) and egocentric (Ego) perspectives to understand daily procedural activities. Inspired by this cognitive ability, in this paper, we propose a novel Unsupervised Ego-Exo Adaptation for Dense Video Captioning (UEA-DVC) task, which aims to predict the time segments and descriptions for target view videos, while only the source view data are labeled during training. Despite previous works endeavoring to address the fully-supervised single-view or cross-view dense video captioning, they lapse in the proposed unsupervised task due to the significant inter-view gap caused by temporal misalignment and irrelevant object interference. Hence, we propose a Gaze Consensus-guided Ego-Exo Adaptation Network (GCEAN) that injects the gaze information into the learned representations for the fine-grained alignment between the Ego and Exo views. Specifically, the Score-based Adversarial Learning Module (SALM) incorporates a discriminative scoring network to learn unified view-invariant representations for bridging distinct views from a global level. Then, the Gaze Consensus Construction Module (GCCM) utilizes gaze representations to progressively calibrate the learned global view-invariant representations for extracting the video temporal contexts based on focusing regions. Moreover, the gaze consensus is constructed via hierarchical gaze-guided consistency losses to spatially and temporally align the source and target views. To support our research, we propose a new EgoMe-UEA-DVC benchmark and experiments demonstrate the effectiveness of our method, which outperforms many related methods by a large margin. The code will be released.
Unsupervised Ego-Exo Adaptation for Dense Video Captioning: A Multi-disciplinary Approach
In this paper, the authors propose a novel task called Unsupervised Ego-Exo Adaptation for Dense Video Captioning (UEA-DVC), which aims to predict time segments and descriptions for target view videos based on only labeled source view data. Previous works in single-view or cross-view dense video captioning have struggled with the task of unsupervised adaptation, mainly due to the inter-view gap caused by temporal misalignment and irrelevant object interference.
To address these challenges, the authors introduce the Gaze Consensus-guided Ego-Exo Adaptation Network (GCEAN). This network incorporates gaze information into the learned representations to achieve fine-grained alignment between the Ego and Exo views. The authors propose two key modules: the Score-based Adversarial Learning Module (SALM) and the Gaze Consensus Construction Module (GCCM).
The SALM module utilizes a discriminative scoring network to learn unified view-invariant representations. By bridging the distinct views at a global level, this module aids in aligning the source and target views. The GCCM module, on the other hand, uses gaze representations to progressively calibrate the learned global view-invariant representations. This calibration is essential for extracting video temporal contexts based on focusing regions.
What sets this approach apart is the incorporation of gaze consensus via hierarchical gaze-guided consistency losses. By spatially and temporally aligning the source and target views, this helps to better understand the relationships between the views and generate accurate captions.
From a multi-disciplinary standpoint, this research combines concepts from computer vision, natural language processing, and cognitive psychology. By exploring the cognitive ability of humans to adapt between exocentric and egocentric perspectives, the authors are able to design a network that mimics this ability. This demonstrates the potential for cross-pollination between different fields of study to advance the development of multimedia information systems.
In terms of its relation to the wider field of multimedia information systems, this work contributes to the advancement of dense video captioning. By addressing the challenges of unsupervised adaptation and incorporating gaze information, the proposed approach improves the accuracy of video captioning in different viewpoints. This has implications for applications such as video summarization, video indexing, and video search, where understanding and generating captions for diverse perspectives is crucial.
The concepts of animations, artificial reality, augmented reality, and virtual realities can also benefit from this research. For example, in augmented reality applications, accurate and contextually relevant captions can enhance users’ understanding and interaction with virtual objects in the real world. Similarly, in virtual reality environments, the ability to generate captions from different viewpoints can enhance the immersive experience and provide more informative narratives.
In conclusion, the Unsupervised Ego-Exo Adaptation for Dense Video Captioning task proposed in this paper, along with the GCEAN network, offers a promising contribution to the field of multimedia information systems. By leveraging the multi-disciplinary nature of the concepts involved, the authors have devised a method that addresses the challenges of unsupervised adaptation and improves the accuracy of video captioning. This research opens up new possibilities for applications in diverse areas such as computer vision, natural language processing, and virtual realities.