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.

Pansharpening, a type of image fusion, combines a high-resolution panchromatic (PAN) image with a low-resolution multispectral (LRMS) image to produce a high-resolution multispectral output. Recent advancements in deep learning have led to the development of data-driven pansharpening methods, which have shown superior performance over traditional non-data-driven approaches. Nevertheless, current data-driven methods primarily focus on the design of single-task-driven networks. They tend to ignore the positive impact that incorporating auxiliary tasks could have on pansharpening. This oversight restricts further improvements in their performance. To break the constraints of single-task methods, we propose a dual-task collaborative promotion network (DCPNet) for pansharpening. DCPNet incorporates an LRMS image super-resolution (SR) reconstruction network into the pansharpening network, establishing a dual-task parallel collaborative framework that achieves joint collaborative optimization for both pansharpening and SR reconstruction. Additionally, we devise a feature fusion scheme that jointly reinforces spectral and spatial details, establishing a bridge for information interaction between the two task branches. This enables the two branches to extract higher quality features in a collaborative way, thus facilitating the realization of the interaction and fusion of dual-task features, as well as the exploration of potential information within the fused features. Experimental results demonstrate that our approach not only effectively transfers spectral information from the LRMS images to the fusion result but also preserves spatial details from the PAN images. The code is available at https://github.com/lhf12278/DCPNet.