Abstract: Logical reasoning is the core of human intelligence and a challenging research topic in the field of artificial intelligence.Human IQ test is one of the common methods to measure the level of human IQ and logical reasoning ability.How to let the computer learn to have the logical reasoning ability similar to human is a very important research content,the purpose is to make the computer from a given image directly learn the logical reasoning mode without having to design a priori reasoning mode for the computer in advance.For this purpose,a new data set Fashion-IQ is proposed.Each sample of the data set contains seven input pictures and a label.The seven pictures are three question input pictures that contain one or more logics,and four option input pictures.The purpose is to let the machine learn to predict the next picture based on the logic contained in the three question input pictures,so as to select the correct option.In order to solve this problem,the temporal relationship model is proposed.For each option,the model first uses a convolutional neural network to extract the spatial features of the first three input pictures and option pictures,and then uses a relation network to combine these four spatial features in pairs.Then,it uses LSTM to extract the first three question input pictures combining the time series feature with the time series feature of this option,the time series feature and the combined space feature are combined to obtain the time series-space fusion feature.Finally,the first three input pictures and the temporal-spatial fusion features obtained from each option are further reasoned,and the softmax function is used for scoring.The option with the highest score is the correct answer.Experiments prove that the model has achieved a relatively high inference accuracy on this data set.

Key words: Inference pattern, IQ test, Logical reasoning, Temporal relation network, Temporal-spatial fusion features