Existing image-text retrieval methods mainly rely on region and word features to measure cross-modal similarities. Thus, dense cross-modal semantic alignment which matches regions and words becomes crucial. However, this is non-trivial due to the heterogeneity gap and the cross-modal attention used to achieve this alignment is inefficient. Towards solving this problem, we propose a novel framework that goes beyond the previous one-tower and two-tower frameworks to learn cross-modal consensus efficiently. The proposed framework does not align regions and words directly like existing methods but uses semantic prototypes as a bridge to attend specific contents with the same semantics among different modalities through semantic decoders, through which cross-modal semantic alignment is naturally achieved. Furthermore, we design a novel plug-and-play self-correction method based on optimal transport to alleviate the drawbacks of incomplete pairwise labels in existing multimodal datasets. On top of various base backbones, we carry out extensive experiments on two benchmark datasets,i.e., Flickr30K and MS-COCO, demonstrating the effectiveness, superiority and generalization of our method.

The mainstream of image and sentence matching studies currently focuses on fine-grained alignment of image regions and sentence words. However, these methods miss a crucial fact: the correspondence between images and sentences does not simply come from alignments between individual regions and words but from alignments between the phrases they form respectively. In this work, we propose a novel Decoupled Cross-modal Phrase-Attention network (DCPA) for image-sentence matching by modeling the relationships between textual phrases and visual phrases. Furthermore, we design a novel decoupled manner for training and inferencing, which is able to release the trade-off for bi-directional retrieval, where image-to-sentence matching is executed in textual semantic space and sentence-to-image matching is executed in visual semantic space. Extensive experimental results on Flickr30K and MS-COCO demonstrate that the proposed method outperforms state-of-the-art methods by a large margin, and can compete with some methods introducing external knowledge.

Scene boundary detection breaks down long videos into meaningful story-telling units and plays a crucial role in high-level video understanding. Despite significant advancements in this area, this task remains a challenging problem as it requires a comprehensive understanding of multimodal cues and high-level semantics. To tackle this issue, we propose a multimodal high-order relation transformer, which integrates a high-order encoder and an adaptive decoder in a unified framework. By modeling the mul-timodal cues and exploring similarities between the shots, the encoder is capable of capturing high-order relations between shots and extracting shot features with context semantics. By clustering the shots adaptively, the decoder can discover more universal switch pattern between successive scenes, thus helping scene boundary detection. Extensive experimental results on three standard benchmarks demonstrate that the proposed model performs favorably against state-of-the-art video scene detection methods.