  Understanding, predicting, and generating object motions and transformations
is a core problem in artificial intelligence. Modeling sequences of evolving
images may provide better representations and models of motion and may
ultimately be used for forecasting, simulation, or video generation.
Diagrammatic Abstract Reasoning is an avenue in which diagrams evolve in
complex patterns and one needs to infer the underlying pattern sequence and
generate the next image in the sequence. For this, we develop a novel
Contextual Generative Adversarial Network based on Recurrent Neural Networks
(Context-RNN-GANs), where both the generator and the discriminator modules are
based on contextual history (modeled as RNNs) and the adversarial discriminator
guides the generator to produce realistic images for the particular time step
in the image sequence. We evaluate the Context-RNN-GAN model (and its variants)
on a novel dataset of Diagrammatic Abstract Reasoning, where it performs
competitively with 10th-grade human performance but there is still scope for
interesting improvements as compared to college-grade human performance. We
also evaluate our model on a standard video next-frame prediction task,
achieving improved performance over comparable state-of-the-art.

Computer Vision and Pattern Recognition;Artificial Intelligence;Machine Learning