In this paper, SER_AMPEL, a multi-source dataset for speech emotion
recognition (SER) is presented. The peculiarity of the dataset is that it is
collected with the aim of providing a reference for speech emotion recognition
in case of Italian older adults. The dataset is collected following different
protocols, in particular considering acted conversations, extracted from movies
and TV series, and recording natural conversations where the emotions are
elicited by proper questions. The evidence of the need for such a dataset
emerges from the analysis of the state of the art. Preliminary considerations
on the critical issues of SER are reported analyzing the classification results
on a subset of the proposed dataset.

本文介绍了 SER_AMPEL，一个用于意大利老年人语音情感识别（SER）的多源数据集。数据集的特点是根据不同的协议进行收集，特别考虑了从电影和电视剧中提取的演奏对话以及通过适当问题引发情感的自然对话。通过对提出的数据集的子集进行分类结果分析，报告了关于 SER 关键问题的初步考虑。

SER_AMPEL: 一个用于意大利老年人 SER 的多源数据集

SER_AMPEL: A multi-source dataset for SER of Italian older adults

One fundamental challenge of vehicle re-identification (re-id) is to learn
robust and discriminative visual representation, given the significant
intra-class vehicle variations across different camera views. As the existing
vehicle datasets are limited in terms of training images and viewpoints, we
propose to build a unique large-scale vehicle dataset (called VehicleNet) by
harnessing four public vehicle datasets, and design a simple yet effective
two-stage progressive approach to learning more robust visual representation
from VehicleNet. The first stage of our approach is to learn the generic
representation for all domains (i.e., source vehicle datasets) by training with
the conventional classification loss. This stage relaxes the full alignment
between the training and testing domains, as it is agnostic to the target
vehicle domain. The second stage is to fine-tune the trained model purely based
on the target vehicle set, by minimizing the distribution discrepancy between
our VehicleNet and any target domain. We discuss our proposed multi-source
dataset VehicleNet and evaluate the effectiveness of the two-stage progressive
representation learning through extensive experiments. We achieve the
state-of-art accuracy of 86.07% mAP on the private test set of AICity
Challenge, and competitive results on two other public vehicle re-id datasets,
i.e., VeRi-776 and VehicleID. We hope this new VehicleNet dataset and the
learned robust representations can pave the way for vehicle re-id in the
real-world environments.

通过构建大规模车辆数据集，并使用一个简单而有效的二阶段渐进学习方法进行学习，本文提出了用于车辆识别的新型 VehicleNet 数据集并实现了优异的识别率。