We present the first systematic study on concealed object detection (COD),
which aims to identify objects that are "perfectly" embedded in their
background. The high intrinsic similarities between the concealed objects and
their background make COD far more challenging than traditional object
detection/segmentation. To better understand this task, we collect a
large-scale dataset, called COD10K, which consists of 10,000 images covering
concealed objects in diverse real-world scenarios from 78 object categories.
Further, we provide rich annotations including object categories, object
boundaries, challenging attributes, object-level labels, and instance-level
annotations. Our COD10K is the largest COD dataset to date, with the richest
annotations, which enables comprehensive concealed object understanding and can
even be used to help progress several other vision tasks, such as detection,
segmentation, classification, etc. Motivated by how animals hunt in the wild,
we also design a simple but strong baseline for COD, termed the Search
Identification Network (SINet). Without any bells and whistles, SINet
outperforms 12 cutting-edge baselines on all datasets tested, making them
robust, general architectures that could serve as catalysts for future research
in COD. Finally, we provide some interesting findings and highlight several
potential applications and future directions. To spark research in this new
field, our code, dataset, and online demo are available on our project page:
this http URL

本研究提出了针对被隐藏物体的检测任务，提供了名为 COD10K 的大型数据集，涵盖 78 个物体类别的 10,000 个来自不同真实场景的图像，并提供了详细的注释。其中，Search Identification Network（SINet）是一种简单但强大的基线用于 COD，并在测试的所有数据集上击败了 12 个前沿基线。

隐蔽物品检测

Concealed Object Detection

In many real-world datasets, like WebVision, the performance of DNN based
classifier is often limited by the noisy labeled data. To tackle this problem,
some image related side information, such as captions and tags, often reveal
underlying relationships across images. In this paper, we present an efficient
weakly supervised learning by using a Side Information Network (SINet), which
aims to effectively carry out a large scale classification with severely noisy
labels. The proposed SINet consists of a visual prototype module and a noise
weighting module. The visual prototype module is designed to generate a compact
representation for each category by introducing the side information. The noise
weighting module aims to estimate the correctness of each noisy image and
produce a confidence score for image ranking during the training procedure. The
propsed SINet can largely alleviate the negative impact of noisy image labels,
and is beneficial to train a high performance CNN based classifier. Besides, we
released a fine-grained product dataset called AliProducts, which contains more
than 2.5 million noisy web images crawled from the internet by using queries
generated from 50,000 fine-grained semantic classes. Extensive experiments on
several popular benchmarks (i.e. Webvision, ImageNet and Clothing-1M) and our
proposed AliProducts achieve state-of-the-art performance. The SINet has won
the first place in the classification task on WebVision Challenge 2019, and
outperformed other competitors by a large margin.

通过使用 SINet 实现弱监督学习，该模型通过引入侧面信息生成紧凑的分类表示并估计每个噪声图像的正确性得分，可以大大减少噪声图像标签的负面影响，并有助于训练具有高性能的 CNN 分类器。