Recently, with the growing popularity of micro-videos, multi-label learning has attracted increasing attention due to its potential commercial value in different scenarios. However, existing methods place more emphasis on the alignment between explicit semantics and visual features, while neglecting the exploration of interactions at fine-grained semantic levels. To address this problem, we propose a novel dual-domain aligned deep hierarchical matrix factorization (DADHMF) method for micro-video multi-label classification. Specifically, we construct a dual-stream deep matrix factorization framework to explore implicit hierarchical semantics and corresponding intrinsic feature representations in top-down and bottom-up ways, respectively. On this basis, we leverage the intralayer alignment strategy to narrow the semantic gap between label and instance domains by introducing adaptive semantic-aware embeddings. Moreover, we further utilize the inverse covariance estimation module to automatically capture latent semantic correlations, and project the structural information into the semantic-aware embeddings to ensure the stability of the intralayer alignment. Extensive experiments on two available micro-video multi-label datasets demonstrate that our proposed method outperforms the state-of-the-art methods.

Deep matrix factorization can be regarded as an extension of traditional matrix factorization to help improve applications like social image tag refinement, image retrieval, and face clustering. Toward this tendency, in this letter, we proposed a low-rank regularized deep collaborative matrix factorization (LRDCMF) method to better tackle micro-video multi-label classification tasks. The proposed method aims to collaboratively learn two sets of factor matrices for characterization of latent attributes and two deep representations for instances and labels, respectively. During factorization process, the inverse covariance constraints are exploited to capture the latent correlation structures among latent attributes and labels and the low-dimensional intrinsic deep representations are ensured by further considering low-rank constraints. Moreover, a triplet term that connects instances representations, label representations, and labels is constructed to increase discrimination power of our method. Experimental results conducted on a large-scale micro-video dataset illustrate our model achieves superior performance in comparison with state-of-the-art methods.