Providing machines with the ability to recognize objects like humans has
always been one of the primary goals of machine vision. The introduction of
RGB-D cameras has paved the way for a significant leap forward in this
direction thanks to the rich information provided by these sensors. However,
the machine vision community still lacks an effective method to synergically
use the RGB and depth data to improve object recognition. In order to take a
step in this direction, we introduce a novel end-to-end architecture for RGB-D
object recognition called recurrent convolutional fusion (RCFusion). Our method
generates compact and highly discriminative multi-modal features by combining
complementary RGB and depth information representing different levels of
abstraction. Extensive experiments on two popular datasets, RGB-D Object
Dataset and JHUIT-50, show that RCFusion significantly outperforms
state-of-the-art approaches in both the object categorization and instance
recognition tasks.

本文提出了一种称为循环卷积融合（RCFusion）的新型 RGB-D 物体识别端到端架构，能够通过组合互补的 RGB 和深度信息表示不同抽象层次的信息来生成紧凑且高度可区分的多模态特征，并在两个流行数据集上的实验中，优于现有的最先进方法。

RGB-D 物体识别的循环卷积融合

Recurrent Convolutional Fusion for RGB-D Object Recognition

To achieve effective and efficient detection of Alzheimer's disease (AD),
many machine learning methods have been introduced into this realm. However,
the general case of limited training samples, as well as different feature
representations typically makes this problem challenging. In this work, we
propose a novel multiple kernel learning framework to combine multi-modal
features for AD classification, which is scalable and easy to implement.
Contrary to the usual way of solving the problem in the dual space, we look at
the optimization from a new perspective. By conducting Fourier transform on the
Gaussian kernel, we explicitly compute the mapping function, which leads to a
more straightforward solution of the problem in the primal space. Furthermore,
we impose the mixed $L_{21}$ norm constraint on the kernel weights, known as
the group lasso regularization, to enforce group sparsity among different
feature modalities. This actually acts as a role of feature modality selection,
while at the same time exploiting complementary information among different
kernels. Therefore it is able to extract the most discriminative features for
classification. Experiments on the ADNI data set demonstrate the effectiveness
of the proposed method.

本论文提出了一种新颖的多核学习框架，通过将多模态特征组合起来进行 AD 分类，提出了一种可伸缩且易于实现的方法，具有挑选最有区分度特征进行分类的能力，并在 ADNI 数据集上进行了实验证明其有效性。