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As a fundamental branch in cross-modal retrieval, image-text retrieval is still a challenging problem largely due to the complementary and imbalanced relationship between different modalities. However, existing works have not effectively scanned and aligned the semantic units distributed in different granularities of images and texts. To address these issues, we propose a dual-branch foreground-background fusion network (FB-Net), which is implemented by fully exploring and fusing the complementarity in semantic units collected from the foreground and background areas of instances (e.g., images and texts). Firstly, to generate multi-granularity semantic units from images and texts, multi-scale semantic scanning is conducted on both foreground and background areas through multi-level overlapped sliding windows. Secondly, to align semantic units between images and texts, the stacked cross-attention mechanism is used to calculate the initial image-text similarity. Thirdly, to further adaptively optimize the image-text similarity, the dynamically self-adaptive weighted loss is designed. Finally, to perform the image-text retrieval, the similarities between multi-granularity foreground and background semantic units are fused to obtain the final image-text similarity. Experimental results show that our proposed FB-Net outperforms representative state-of-the-art methods for image-text retrieval, and ablation studies further verify the effectiveness of each component in FB-Net.

Image-text retrieval aims to search related results of one modality by querying another modality. As a fundamental and key problem in cross-modal retrieval, image-text retrieval is still a challenging problem owing to the complementary and imbalanced relationship between different modalities (i.e., Image and Text) and different granularities (i.e., Global-level and Local-level). However, existing works have not fully considered how to effectively mine and fuse the complementarities between images and texts at different granularities. Therefore, in this paper, we propose a hierarchical adaptive alignment network, whose contributions are as follows: (1) We propose a multi-level alignment network, which simultaneously mines global-level and local-level data, thereby enhancing the semantic association between images and texts. (2) We propose an adaptive weighted loss to flexibly optimize the image-text similarity with two stages in a unified framework. (3) We conduct extensive experiments on three public benchmark datasets (Corel 5K, Pascal Sentence, and Wiki) and compare them with eleven state-of-the-art methods. The experimental results thoroughly verify the effectiveness of our proposed method.

This study aimed to evaluate the effects of delayed femoral vein ligation on the clinical outcomes of hip disarticulation. We retrospectively reviewed 20 patients with extremity tumors (10 bone tumors and 10 soft tissue sarcomas [STS]) who underwent hip disarticulation. Patients treated for hip disarticulation with synchronous femoral vein ligation (n = 10, regular surgery group) and hip disarticulation with delayed femoral vein ligation (n = 10, delayed ligation group), respectively, were enrolled in this study. The operative time and blood loss were used to evaluate the clinical outcomes. The delayed ligation group had significantly lower operative times than the regular surgery group (P < 0.05). Total, hidden, and intraoperative blood loss were all significantly lower in the delayed ligation group than in the regular surgery group (P < 0.05). However, there were no significant differencesin postoperative bloodloss. In conclusion, delayed femoral vein ligation could significantly reduce the operative time, hidden bloodloss, andintraoperative blood loss in patients undergoing hip disarticulation. [ABSTRACT FROM AUTHOR]