Labelled image datasets have played a critical role in high-level image
understanding. However, the process of manual labelling is both time-consuming
and labor intensive. To reduce the cost of manual labelling, there has been
increased research interest in automatically constructing image datasets by
exploiting web images. Datasets constructed by existing methods tend to have a
weak domain adaptation ability, which is known as the "dataset bias problem".
To address this issue, we present a novel image dataset construction framework
that can be generalized well to unseen target domains. Specifically, the given
queries are first expanded by searching the Google Books Ngrams Corpus to
obtain a rich semantic description, from which the visually non-salient and
less relevant expansions are filtered out. By treating each selected expansion
as a "bag" and the retrieved images as "instances", image selection can be
formulated as a multi-instance learning problem with constrained positive bags.
We propose to solve the employed problems by the cutting-plane and
concave-convex procedure (CCCP) algorithm. By using this approach, images from
different distributions can be kept while noisy images are filtered out. To
verify the effectiveness of our proposed approach, we build an image dataset
with 20 categories. Extensive experiments on image classification,
cross-dataset generalization, diversity comparison and object detection
demonstrate the domain robustness of our dataset.

本论文提出了一种基于多实例学习和切平面算法的图像数据集构建框架，可以有效应对数据集偏见问题，提高数据集的领域适应性，并通过广泛实验验证了该方法的有效性。

利用网络图像进行数据集构建：一种领域鲁棒的方法

Exploiting Web Images for Dataset Construction: A Domain Robust Approach

Since its beginning visual recognition research has tried to capture the huge
variability of the visual world in several image collections. The number of
available datasets is still progressively growing together with the amount of
samples per object category. However, this trend does not correspond directly
to an increasing in the generalization capabilities of the developed
recognition systems. Each collection tends to have its specific characteristics
and to cover just some aspects of the visual world: these biases often narrow
the effect of the methods defined and tested separately over each image set.
Our work makes a first step towards the analysis of the dataset bias problem on
a large scale. We organize twelve existing databases in a unique corpus and we
present the visual community with a useful feature repository for future
research.

本文开展了针对视觉图像识别中数据集偏见问题的大规模分析，通过将 12 个已有数据库组织成一个语料库，提供了一个有用的特征储存库以供未来研究使用。