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Often, deep network models are purely inductive during training and whileperforming inference on unseen data. Thus, when such models are used forpredictions, it is well known that they often fail to capture the semanticinformation and implicit dependencies that exist among objects (or concepts) ona population level. Moreover, it is still unclear how domain or prior modalknowledge can be specified in a backpropagation friendly manner, especially inlarge-scale and noisy settings. In this work, we propose an end-to-end visionand language model incorporating explicit knowledge graphs. We also introducean interactive out-of-distribution (OOD) layer using implicit network operator.The layer is used to filter noise that is brought by external knowledge base.In practice, we apply our model on several vision and language downstream tasksincluding visual question answering, visual reasoning, and image-text retrievalon different datasets. Our experiments show that it is possible to designmodels that perform similarly to state-of-art results but with significantlyfewer samples and training time.

Differentiable Outlier Detection Enable Robust Deep Multimodal Analysis