Video captioning aims to describe the content of a given video with condensed natural language sentences. Such a captioning task is full of challenges since the high requirements for visual-textual relevance and multimodal fusion understanding. Previous works primarily focus on visual content modeling, often overlooking the rich semantic correlations between visual and textual modalities, which results in incomplete understanding of the multimodal context and suboptimal caption accuracy. In this paper, we propose a multimodal graph conditioned diffusion model for video captioning, named MGCDVc. The idea behind our model is to incorporate graph-based relational reasoning with diffusion-based generative modeling to jointly model cross-modal relationships and capture latent semantic structure. Specifically, we learn a set of latent concept anchors to bridge the visual and textual modality nodes, enabling the construction of a weighted multimodal graph. Then we introduce the graph conditioned diffusion strategy which generates the textual semantic nodes and associated edges under the graph structure awareness condition. Furthermore, a soft pruning mechanism is designed to filter out low-quality nodes, thus further refining the generated multimodal graph to provide more accurate semantic structural guidance for caption generation. Experimental results on several popular datasets demonstrate that our model achieves better performance in video captioning task.

The emergence of video captioning makes it possible to automatically generate natural language description for a given video. However, generating detailed video descriptions that incorporate domain-specific information remains an unsolved challenge, holding significant research and application value, particularly in domains such as sports commentary generation. Moreover, sports event commentary goes beyond being a mere game report, it involves entertaining, metaphorical, and emotional descriptions. To promote the field of sports commentary automatic generation, in this paper, we introduce a novel dataset, the Basketball Highlight Commentary (BH-Commentary), comprising approximately 4K basketball highlight videos with groundtruth commentaries from professional commentators. In addition, we propose an end-to-end framework as a benchmark for basketball highlight commentary generation task, in which a lightweight and effective prompt strategy is designed to enhance alignment fusion among visual and textual features. Experimental results on the BH-Commentary dataset demonstrate the validity of the dataset and the effectiveness of the proposed benchmark for sports highlight commentary generation.

Many complex systems derived from nature and society consist of multiple types of entities and heterogeneous interactions, which can be effectively modeled as heterogeneous information network (HIN). Structural analysis of heterogeneous networks is of great significance by leveraging the rich semantic information of objects and links in the heterogeneous networks. And, clustering heterogeneous networks aims to group vertices into classes, which sheds light on revealing the structure–function relations of the underlying systems. The current algorithms independently perform the feature extraction and clustering, which are criticized for not fully characterizing the structure of clusters. In this study, we propose a learning model by joint Graph Embedding and Nonnegative Matrix Factorization (aka GEjNMF ), where feature extraction and clustering are simultaneously learned by exploiting the graph embedding and latent structure of networks. We formulate the objective function of GEjNMF and transform the heterogeneous network clustering problem into a constrained optimization problem, which is effectively solved by l 0 -norm optimization. The advantage of GEjNMF is that features are selected under the guidance of clustering, which improves the performance and saves the running time of algorithms at the same time. The experimental results on three benchmark heterogeneous networks demonstrate that GEjNMF achieves the best performance with the least running time compared with the best state-of-the-art methods. Furthermore, the proposed algorithm is robust across heterogeneous networks from various fields. The proposed model and method provide an effective alternative for heterogeneous network clustering.