  Variable scene layouts and coexisting objects across scenes make indoor scene
recognition still a challenging task. Leveraging object information within
scenes to enhance the distinguishability of feature representations has emerged
as a key approach in this domain. Currently, most object-assisted methods use a
separate branch to process object information, combining object and scene
features heuristically. However, few of them pay attention to interpretably
handle the hidden discriminative knowledge within object information. In this
paper, we propose to leverage discriminative object knowledge to enhance scene
feature representations. Initially, we capture the object-scene discriminative
relationships from a probabilistic perspective, which are transformed into an
Inter-Object Discriminative Prototype (IODP). Given the abundant prior
knowledge from IODP, we subsequently construct a Discriminative Graph Network
(DGN), in which pixel-level scene features are defined as nodes and the
discriminative relationships between node features are encoded as edges. DGN
aims to incorporate inter-object discriminative knowledge into the image
representation through graph convolution and mapping operations (GCN). With the
proposed IODP and DGN, we obtain state-of-the-art results on several widely
used scene datasets, demonstrating the effectiveness of the proposed approach.
