Query-Dependent Video Representation for Moment Retrieval and Highlight Detection (2303.13874v1)

Abstract: Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is to localize the moment and estimate clip-wise accordance level, i.e., saliency score, to the given text query. Although the recent transformer-based models brought some advances, we found that these methods do not fully exploit the information of a given query. For example, the relevance between text query and video contents is sometimes neglected when predicting the moment and its saliency. To tackle this issue, we introduce Query-Dependent DETR (QD-DETR), a detection transformer tailored for MR/HD. As we observe the insignificant role of a given query in transformer architectures, our encoding module starts with cross-attention layers to explicitly inject the context of text query into video representation. Then, to enhance the model's capability of exploiting the query information, we manipulate the video-query pairs to produce irrelevant pairs. Such negative (irrelevant) video-query pairs are trained to yield low saliency scores, which in turn, encourages the model to estimate precise accordance between query-video pairs. Lastly, we present an input-adaptive saliency predictor which adaptively defines the criterion of saliency scores for the given video-query pairs. Our extensive studies verify the importance of building the query-dependent representation for MR/HD. Specifically, QD-DETR outperforms state-of-the-art methods on QVHighlights, TVSum, and Charades-STA datasets. Codes are available at github.com/wjun0830/QD-DETR.

• The paper introduces QD-DETR, a novel detection transformer that uses cross-attentive encoding to merge text queries with video content effectively.
• It employs a negative pair learning strategy and an input-adaptive saliency predictor to enhance the distinction between relevant and non-relevant video segments.
• Empirical results on QVHighlights, TVSum, and Charades-STA datasets demonstrate superior precision and recall, setting a new benchmark for video moment retrieval and highlight detection.

Overview of Query-Dependent Video Representation for Moment Retrieval and Highlight Detection

The paper presents a novel approach to video moment retrieval and highlight detection (MR/HD), addressing the challenge of effectively utilizing text queries to discern relevant segments within video content. This problem has gained substantial attention due to the growing demand for sophisticated video understanding capabilities. The authors introduce Query-Dependent DETR (QD-DETR), a detection transformer that innovatively integrates text queries into the video representation process, aiming to enhance the localization and saliency scoring of video clips in alignment with given textual descriptions.

Key Contributions

1. Query-Dependent Encoding: The paper highlights a gap in existing transformer-based models where the relevance of text queries to video content is not fully leveraged. They propose a cross-attentive transformer encoder that uses cross-attention layers initially to incorporate context from the text query directly into the video representation. This is a critical departure from previous approaches that typically merge modalities in a less structured manner.
2. Negative Pair Learning: To augment the distinction between relevant and non-relevant video-query pairs, the authors introduce a novel learning strategy. By creating negative video-query pairs (using unrelated queries), the model is trained to assign low saliency scores to these pairs, effectively sharpening its ability to distinguish relevant content.
3. Input-Adaptive Saliency Predictor: The paper introduces a saliency token that functions dynamically, adapting to different video-query pairs to predict saliency. This approach mitigates the limitation of using a static predictor head, allowing more nuanced and context-sensitive saliency estimation.
4. Empirical Validation: Extensive experiments demonstrate that QD-DETR substantially outperforms state-of-the-art models across QVHighlights, TVSum, and Charades-STA datasets. Notably, the model excels in criteria involving high intersection over union (IoU) thresholds, indicating superior precise localization capabilities. These datasets are quintessential benchmarks in the video retrieval and summarization landscape.

Implications and Future Directions

The QD-DETR model sets a new bar for handling multi-modal information in MR/HD tasks. Its superior performance in both recall and precision metrics suggests that enhancing query dependency in video representation is a potent strategy. These findings implicitly challenge future model architectures to focus more on the nuanced interactions between video content and associated textual queries rather than treating them as separate or superficially integrated entities.

In practical terms, the proposed model could revolutionize applications where quick and accurate video parsing is essential, such as content recommendation, educational video indexing, and security footage analysis. From a theoretical standpoint, this reinforces the potential of transformer architectures to handle cross-modal integration tasks when designed with specific modality interactions in mind.

Future developments in AI may build upon this work by exploring even more sophisticated means of query integration, such as dynamically evolving queries based on video content or incorporating additional modalities like user interaction feedback. Additionally, expanding the proposed model to work efficiently with diverse video source qualities and lengths without significant performance degradation would be a compelling avenue for further research.