This is NIFS 2016 spotlight video for multimodal-lateral network for Visual QA. Hi, I'm Jin Hua, I'm just Jin, as all national university. Visual question answering is a task which needs visual information while question answering using natural language. So multimodal learning is important to solve these kind of problems. Look at this example. Green answer shows human responses look at the image, while blue answers didn't. Therefore blue answers may utilize their own background knowledge answering with a diamond or round to the second question. Our approach is based on the study of deep residual learning by Hian, his colleagues. They suggested a simple method to effectively and efficiently stack insanely deep neural network with hundreds of layers using residual learning blocks. This schematic diagram shows the gay idea of zoom that I did the mapping of X, effectively obligatory to design mapping H of X. The minimum mapping I thought of X is to optimize the residual mapping hopefully than the original mapping. How can you apply this idea to multimodal task? Firstly, we need a reasonable mapping for a bunch of models' inputs. We notice that a hard-to-fertil product of embedded vectors is a problem method to learn multimodal representations. The next is choice of shock connections for multimodal inputs. Many empirical observations suggest to use a connection vector for a shock connection. However, we need to confirm this choice after exploring other options. We carefully select some more alternative models. The proposed model structure is B. The full experiment research can be found in the paper. This slide shows the proposed model using 3 block layers, which is so known on one to 4 block layered models. We call it MLN. We use the skipped effectors of Kero-C et al. 2015, which provided three trained models of word embeddings and GILUs, percussion feature vectors, and redness for visual feature vectors. MLN significantly outperforms previous methods proposed upended and multiple choice tasks at the time of some mention. However, the performance of number and other types are still dissatisfied compared to human performance, though the advances in the recent works was mainly for other type answers. This fact motivates to study on county mechanism in future work. We propose a noble method to visualize a tense effect in used by how to map further to the model, though there is no explicit tense mechanism. There are the examples using the proposed visual legend methods. Sometimes tense effect are increasingly concentrating on the subject, when we can use more examples in appendix. Moreover, I want to share our recent study on visual care sensoring tasks. We analyzed joint resume mapping in mRNA as a world-ranked balloon pulling, achieving new state water-owned VQD dataset, a bit labeled in archive preprint and under the review in IClayer 2017.