To model how a human would annotate an image is an important and interesting task relevant to image captioning. Its main challenge is that a same visual concept may be important in some images but becomes less salient in other situations. Further, the subjective viewpoints of a human annotator also play a crucial role in finalizing the annotations. To deal with such high variability, we introduce a new deep net model that integrates a CNN with a variational auto-encoder (VAE). With the latent features embedded in a VAE, it becomes more flexible to tackle the uncertainly of human-centric annotations. On the other hand, the supervised generalization further enables the discriminative power of the generative VAE model. The resulting model can be end-to-end fine-tuned to further improve the performance on predicting visual concepts. The provided experimental results show that our method is state-of-the-art over two benchmark datasets: MS COCO and Flickr30K, producing mAP of 36.6 and 23.49, and PHR (Precision at Human Recall) of 49.9 and 32.04, respectively.