Our work focuses on tackling the problem of fine-grained recognition with incomplete multi-modal data, which is overlooked by previous work in the literature. It is desirable to not only capture fine-grained patterns of objects but also alleviate the challenges of missing modalities for such a practical problem. In this paper, we propose to leverage a meta-learning strategy to learn model abilities of both fast modal adaptation and more importantly missing modality completion across a variety of incomplete multi-modality learning tasks. Based on that, we develop a meta-completion method, termed as MECOM, to perform multimodal fusion and explicit missing modality completion by our proposals of cross-modal attention and decoupling reconstruction. To further improve fine-grained recognition accuracy, an additional partial stream (as a counterpart of the main stream of MECOM, i.e., holistic) and the part-level features (corresponding to fine-grained objects' parts) selection are designed, which are tailored for fine-grained nature to capture discriminative but subtle part-level patterns. Comprehensive experiments from quantitative and qualitative aspects, as well as various ablation studies, on two fine-grained multimodal datasets and one generic multimodal dataset show our superiority over competing methods. Our code is open-source and available at https://github.com/SEU-VIPGroup/MECOM.

Link prediction is a fundamental problem in network data analysis, which has attracted increasing attention from many fields, with many new advances. In particular, many structure similarity-based algorithms that only use network topology information have been applied because of their simple framew ork. The prediction accuracy of these methods depends on the compatibility between the algorithm definition and the structural characteristics of the target network, so the stability of structure similarity-based algorithms is low. Thus, these algorithms may obtain good results with some networks but fail with others. Given the ambiguous relationships between these algorithms, we propose a Choquet fuzzy integral-based link prediction method, which integrates structure similarity-based algorithms via the Choquet fuzzy integral to improve the prediction accuracy and achieve more stable prediction performance. Empirical experiments using six real networks demonstrated that the proposed method outperformed the mainstream link prediction baseline methods.