  In this paper, we rethink how a DNN encodes visual concepts of different
complexities from a new perspective, i.e. the game-theoretic multi-order
interactions between pixels in an image. Beyond the categorical taxonomy of
objects and the cognitive taxonomy of textures and shapes, we provide a new
taxonomy of visual concepts, which helps us interpret the encoding of shapes
and textures, in terms of concept complexities. In this way, based on
multi-order interactions, we find three distinctive signal-processing behaviors
of DNNs encoding textures. Besides, we also discover the flexibility for a DNN
to encode shapes is lower than the flexibility of encoding textures.
Furthermore, we analyze how DNNs encode outlier samples, and explore the
impacts of network architectures on interactions. Additionally, we clarify the
crucial role of the multi-order interactions in real-world applications. The
code will be released when the paper is accepted.
