We investigated DNNs as encoding models of visual areas V1, V2, V4, and IT. We examined 32 visual deep neural networks.

In most applications, the output labels of learning problems have some structure that is crucial to consider. This includes natural language processing applications, where the output may be a sentence, a sequence of parts-of-speech tags, a parse tree, or a dependency graph.

Along with the dataset, two tasks are proposed to evaluate machines'

Transformer decoder has to predict all tokens at once, conditioned only on the image.