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. 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.

本文提出了一种利用判别性物体知识增强场景特征表示的方法，并通过构建判别性图网络（DGN）以及生成物体场景辨别原型（IODP），将物体知识融入图像表示中，从而在多个常用场景数据集上取得了最先进的结果，证明了该方法的有效性。