Video-text retrieval aims to precisely search for videos most relevant to text queries within a video corpus. However, existing methods are largely limited to single-text (single-event) queries and are not effective at handling multi-text (multi-event) queries. Furthermore, these methods typically focus solely on retrieval and do not attempt to locate multiple events within the retrieved videos. To address these limitations, our paper proposes a novel method named Disentangling Inter- and Intra-Video Relations, which jointly addresses multi-event video-text retrieval and grounding. This method leverages both inter-video and intra-video event relationships to enhance retrieval and grounding performance. At the retrieval level, we devise a Relational Event-Centric Video-Text Retrieval module based on the principle that comprehensive textual information leads to precise correspondence between text and video. It incorporates event relationship features at different hierarchical levels and exploits the hierarchical structure of video relationships to achieve multi-level contrastive learning between events and videos. This approach enhances the richness, accuracy, and comprehensiveness of event descriptions, improving alignment precision between text and video and enabling effective differentiation among videos. For event grounding, we propose Event Contrast-Driven Video Grounding, which accounts for positional differences among events on the 2D temporal score map and achieves precise grounding of multiple events through divergence learning for their locations. Our solution not only provides efficient text-to-video retrieval but also accurately grounds events within the retrieved videos, addressing the shortcomings of existing methods. Extensive experimental results on the ActivityNet Captions and Charades-STA benchmark datasets demonstrate the superior performance of our method, validating its effectiveness. The innovation of this research lies in introducing a new joint framework for video-text retrieval and multi-event grounding while offering new ideas for further research and applications in related fields. The code is available at https://github.com/X7J92/MVT-RG.

Video Paragraph Grounding (VPG) aims to precisely locate the most appropriate moments within a video that are relevant to a given textual paragraph query. However, existing methods typically rely on large-scale annotated temporal labels and assume that the correspondence between videos and paragraphs is known. This is impractical in real-world applications, as constructing temporal labels requires significant labor costs, and the correspondence is often unknown. To address this issue, we propose a Dual-task Mutual Reinforcing Embedded Joint Video Paragraph Retrieval and Grounding method (DMR-JRG). In this method, retrieval and grounding tasks are mutually reinforced rather than being treated as separate issues. DMR-JRG mainly consists of two branches: a retrieval branch and a grounding branch. The retrieval branch uses inter-video contrastive learning to roughly align the global features of paragraphs and videos, reducing modality differences and constructing a coarse-grained feature space to break free from the need for correspondence between paragraphs and videos. Additionally, this coarse-grained feature space further facilitates the grounding branch in extracting fine-grained contextual representations. In the grounding branch, we achieve precise cross-modal matching and grounding by exploring the consistency between local, global, and temporal dimensions of video segments and textual paragraphs. By synergizing these dimensions, we construct a fine-grained feature space for video and textual features, greatly reducing the need for large-scale annotated temporal labels. Meanwhile, we design a grounding reinforcement retrieval module (GRRM) that brings the coarse-grained feature space of the retrieval branch closer to the fine-grained feature space of the grounding branch, thereby reinforcing retrieval branch through grounding branch, and finally achieving mutual reinforcement between tasks. Extensive experiments on three challenging datasets demonstrate the effectiveness of our proposed method. The code is available athttps://github.com/X7J92/DMR-JRG.

Visual grounding has attracted wide attention thanks to its broad application in various visual language tasks. Although visual grounding has made significant research progress, existing methods ignore the promotion effect of the association between text and image features at different hierarchies on cross-modal matching. This paper proposes a Phrase Decoupling Cross-Modal Hierarchical Matching and Progressive Position Correction Visual Grounding method. It first generates a mask through decoupled sentence phrases, and a text and image hierarchical matching mechanism is constructed, highlighting the role of association between different hierarchies in cross-modal matching. In addition, a corresponding target object position progressive correction strategy is defined based on the hierarchical matching mechanism to achieve accurate positioning for the target object described in the text. This method can continuously optimize and adjust the bounding box position of the target object as the certainty of the text description of the target object improves. This design explores the association between features at different hierarchies and highlights the role of features related to the target object and its position in target positioning. The proposed method is validated on different datasets through experiments, and its superiority is verified by the performance comparison with the state-of-the-art methods.