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。

室内场景识别的物体间判别图建模

Inter-object Discriminative Graph Modeling for Indoor Scene Recognition

Exploring the semantic context in scene images is essential for indoor scene
recognition. However, due to the diverse intra-class spatial layouts and the
coexisting inter-class objects, modeling contextual relationships to adapt
various image characteristics is a great challenge. Existing contextual
modeling methods for indoor scene recognition exhibit two limitations: 1)
During training, space-independent information, such as color, may hinder
optimizing the network's capacity to represent the spatial context. 2) These
methods often overlook the differences in coexisting objects across different
scenes, suppressing the performance of scene recognition. To address these
limitations, we propose SpaCoNet, a novel approach that simultaneously models
the Spatial relation and Co-occurrence of objects based on semantic
segmentation. Firstly, the semantic spatial relation module (SSRM) is designed
to explore the spatial relations among objects within a scene. With the help of
semantic segmentation, this module decouples the spatial information from the
image, effectively avoiding the influence of irrelevant features. Secondly,
both spatial context features from SSRM and deep features from RGB feature
extractor are used to distinguish the coexisting object across different
scenes. Finally, utilizing the discriminative features mentioned above, we
employ the self-attention mechanism to explore the long-range co-occurrence
relationships among objects, and further generate a semantic-guided feature
representation for indoor scene recognition. Experimental results on three
publicly available datasets demonstrate the effectiveness and generality of the
proposed method. The code will be made publicly available after the
blind-review process is completed.

本文提出了一种基于语义分割的新方法 SpaCoNet，旨在同时模拟对象的空间关系和共现情况，进而在室内场景识别任务中生成语义引导的特征表示，该方法具有良好的通用性和效果。

室内场景识别的语义引导上下文建模

Semantic-guided context modeling for indoor scene recognition

Despite the remarkable success of convolutional neural networks in various
computer vision tasks, recognizing indoor scenes still presents a significant
challenge due to their complex composition. Consequently, effectively
leveraging semantic information in the scene has been a key issue in advancing
indoor scene recognition. Unfortunately, the accuracy of semantic segmentation
has limited the effectiveness of existing approaches for leveraging semantic
information. As a result, many of these approaches remain at the stage of
auxiliary labeling or co-occurrence statistics, with few exploring the
contextual relationships between the semantic elements directly within the
scene. In this paper, we propose the Semantic Region Relationship Model (SRRM),
which starts directly from the semantic information inside the scene.
Specifically, SRRM adopts an adaptive and efficient approach to mitigate the
negative impact of semantic ambiguity and then models the semantic region
relationship to perform scene recognition. Additionally, to more
comprehensively exploit the information contained in the scene, we combine the
proposed SRRM with the PlacesCNN module to create the Combined Semantic Region
Relation Model (CSRRM), and propose a novel information combining approach to
effectively explore the complementary contents between them. CSRRM
significantly outperforms the SOTA methods on the MIT Indoor 67, reduced
Places365 dataset, and SUN RGB-D without retraining. The code is available at:
this https URL

该研究使用语义信息建立了 “语义区域关系模型”（SRRM），并将其与 PlacesCNN 模块相结合，在 MIT Indoor 67，reduced Places365 数据集和 SUN RGB-D 等数据集上展示了显著的性能优势。