DF2Net：RGB-D室内场景分类的深度特征学习与融合网络

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"DF2Net: ADiscriminative Feature Learning and Fusion Network for RGB-D Indoor Scene Classiﬁcation" 本文深入探讨了RGB-D室内场景分类这一领域，这是一个由于其复杂性和多样性而极具挑战性的任务。室内场景可能包含各种各样的物体和布局，这使得学习一种能够鲁棒地表示场景的特征变得尤为困难。此外，RGB图像提供了丰富的颜色和纹理信息，而Depth图像则提供了空间结构信息，但这两者之间的语义鸿沟使得有效地融合这两种模态的数据成为一大难题。现有的方法通常采用带有softmax损失的深度网络来学习分类表示，然后简单地将RGB和Depth的特征串联起来进行融合。然而，这种方法并未充分考虑类内和类间的相似性，也没有深入挖掘不同模态之间的内在关系。为了解决这些问题，文章提出了一种名为DF2Net的区分性特征学习和融合网络，该网络分为两个阶段进行训练。在第一阶段，DF2Net构建了一个深度的多任务网络，同时最小化结构化损失和softmax损失。这种结构化的损失有助于捕获场景的几何结构，而softmax损失则有助于分类任务。通过这种方式，每个模态的场景表示可以得到优化，使其更能反映场景的特性。在第二阶段，DF2Net引入了一个创新的判别式融合网络。这个网络旨在学习模态间的相关特征以及每个模态的独特特征。它能够区分不同模态的特征，同时捕捉它们之间的关联性，从而弥合RGB和Depth之间的语义鸿沟。通过在SUN RGB-D数据集和NYU深度数据集V2上的广泛分析和实验，DF2Net证明了其在RGB-D室内场景分类任务上的优越性能，超过了其他现有的最新方法。这些实验证明了DF2Net在处理模态融合和场景理解问题上的有效性，为RGB-D场景理解的研究开辟了新的方向。 DF2Net的贡献在于其两阶段的训练策略，它不仅强化了单模态的特征学习，还通过判别式融合机制有效地整合了多模态信息。这对于未来在复杂环境下的视觉感知和理解任务具有重要的启示意义，尤其是在机器人导航、智能家居和增强现实等领域。

Net: A Discriminative Feature Learning and Fusion Network for RGB-D

Indoor Scene Classiﬁcation

Yabei Li

1,2

, Junge Zhang

1,2

, Yanhua Cheng

, Kaiqi Huang

1,2,4

, Tieniu Tan

1,2,4

CRIPAC & NLPR, CASIA

University of Chinese Academy of Sciences

Tencent Wechat AI

CAS Center for Excellence in Brain Science and Intelligence Technology

yabei.li@cripac.ia.ac.cn, {jgzhang, kqhuang, tnt}@nlpr.ia.ac.cn, breezecheng@tencent.com

Abstract

This paper focuses on the task of RGB-D indoor scene clas-

siﬁcation. It is a very challenging task due to two folds. 1)

Learning robust representation for indoor scene is difﬁcult

because of various objects and layouts. 2) Fusing the com-

plementary cues in RGB and Depth is nontrivial since there

are large semantic gaps between the two modalities. Most ex-

isting works learn representation for classiﬁcation by training

a deep network with softmax loss and fuse the two modali-

ties by simply concatenating the features of them. However,

these pipelines do not explicitly consider intra-class and inter-

class similarity as well as inter-modal intrinsic relationships.

To address these problems, this paper proposes a Discrimi-

native Feature Learning and Fusion Network (DF

Net) with

two-stage training. In the ﬁrst stage, to better represent scene

in each modality, a deep multi-task network is constructed to

simultaneously minimize the structured loss and the softmax

loss. In the second stage, we design a novel discriminative

fusion network which is able to learn correlative features of

multiple modalities and distinctive features of each modality.

Extensive analysis and experiments on SUN RGB-D Dataset

and NYU Depth Dataset V2 show the superiority of DF

Net

over other state-of-the-art methods in RGB-D indoor scene

classiﬁcation task.

Introduction

Scene classiﬁcation is one of the basic problems in computer

vision research. Recently, with the release of cost-affordable

depth sensors, e.g. Kinect, which provide strong illumina-

tion and color invariant geometric cues, some intrinsic chal-

lenges in indoor scene classiﬁcation such as various illumi-

nation, diverse objects and layouts are promising to be par-

tially solved.

Compared with the standard object-centric image classi-

ﬁcation problem, the task of RGB-D indoor scene classiﬁ-

cation has several challenges. Firstly, obtaining robust rep-

resentation for scene classiﬁcation in single modality is dif-

ﬁcult. To understand a scene, people not only recognize the

objects in the scene, but also consider the correlations of the

objects. As for indoor scenes, they are usually cluttered with

diverse objects and various layouts, resulting in large intra-

class variation and sever inter-class overlap. As we illustrat-

ed in Figure 1, the classroom has various views. Some view

 2018, Association for the Advancement of Artiﬁcial

Figure 1: The difﬁculties of RGB-D indoor scene classiﬁca-

tion. 1) indoor scene images have large intra-class variation

and small inter-class variation. 2) RGB and Depth image

have semantic gaps. Sample images are from SUN RGB-D

Dataset.

of classroom is similar to other scene categories such as lec-

ture theatre. Secondly, although it’s an opportunity to utilize

the additional depth cues to beneﬁt indoor scene classiﬁca-

tion, there are large semantic gaps between the RGB and

Depth modality. As shown in Figure 1, RGB image gives

appearance cues while Depth image provides geometric pri-

ors. How to exhaustively use the complementary cues in the

RGB and Depth modalities remains an open problem.

The ideal multimodal representation for RGB-D indoor

scene ought to have small distances between the same class

and large distances between the different classes, as shown

in Figure 2(a). One of the most popular pipelines (Eitel et al.

2015) is to learn representation for RGB and Depth image

with softmax loss separately and directly concatenate them,

illustrated as in Figure 2(b). However, for scene images that

have large intra-class variation and small inter-class vari-

ation, it’s hard to obtain discriminative enough RGB and

Depth representation. It leads to the concatenated multi-

modal representation far from ideal. To ease this situation,

we design a discriminative feature learning network which

can explicitly model the intra-class and inter-class similarity

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