We study the problem of object recognition for categories for which we have no training examples, a task also called zero-data or zero-shot learning. This situation has hardly been studied in computer vision research, even though it occurs frequently; the world contains tens of thousands of different object classes, and image collections have been formed and suitably annotated for only a few of them. To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object's color or shape. Because the identification of each such property transcends the specific learning task at hand, the attribute classifiers can be prelearned independently, for example, from existing image data sets unrelated to the current task. Afterward, new classes can be detected based on their attribute representation, without the need for a new training phase. In this paper, we also introduce a new data set, Animals with Attributes, of over 30,000 images of 50 animal classes, annotated with 85 semantic attributes. Extensive experiments on this and two more data sets show that attribute-based classification indeed is able to categorize images without access to any training images of the target classes.

The PASCAL Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the chal-

广义零射击学习（GZSL）旨在培训一个模型，以在某些输出类别在监督学习过程中未知的情况下对数据样本进行分类。为了解决这一具有挑战性的任务，GZSL利用可见的（源）和看不见的（目标）类的语义信息来弥合所见类和看不见的类之间的差距。自引入以来，已经制定了许多GZSL模型。在这篇评论论文中，我们介绍了有关GZSL的全面评论。首先，我们提供了GZSL的概述，包括问题和挑战。然后，我们为GZSL方法介绍了分层分类，并讨论了每个类别中的代表性方法。此外，我们讨论了GZSL的可用基准数据集和应用程序，以及有关研究差距和未来研究方向的讨论。

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to wellinformed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.

场景分类已确定为一个具有挑战性的研究问题。与单个对象的图像相比，场景图像在语义上可能更为复杂和抽象。它们的差异主要在于识别的粒度水平。然而，图像识别是场景识别良好表现的关键支柱，因为从对象图像中获得的知识可用于准确识别场景。现有场景识别方法仅考虑场景的类别标签。但是，我们发现包含详细的本地描述的上下文信息也有助于允许场景识别模型更具歧视性。在本文中，我们旨在使用对象中编码的属性和类别标签信息来改善场景识别。基于属性和类别标签的互补性，我们提出了一个多任务属性识别识别（MASR）网络，该网络学习一个类别嵌入式，同时预测场景属性。属性采集和对象注释是乏味且耗时的任务。我们通过提出部分监督的注释策略来解决该问题，其中人类干预大大减少。该策略为现实世界情景提供了更具成本效益的解决方案，并且需要减少注释工作。此外，考虑到对象检测到的分数所指示的重要性水平，我们重新进行了权威预测。使用提出的方法，我们有效地注释了四个大型数据集的属性标签，并系统地研究场景和属性识别如何相互受益。实验结果表明，与最先进的方法相比

Despite progress in perceptual tasks such as image classification, computers still perform poorly on cognitive tasks such as image description and question answering. Cognition is core to tasks that involve not just recognizing, but reasoning about our visual world. However, models used to tackle the rich content in images for cognitive tasks are still being trained using the same datasets designed for perceptual tasks. To achieve success at cognitive tasks, models need to understand the interactions and relationships between objects in

即使机器学习算法已经在数据科学中发挥了重要作用，但许多当前方法对输入数据提出了不现实的假设。由于不兼容的数据格式，或数据集中的异质，分层或完全缺少的数据片段，因此很难应用此类方法。作为解决方案，我们提出了一个用于样本表示，模型定义和培训的多功能，统一的框架，称为“ Hmill”。我们深入审查框架构建和扩展的机器学习的多个范围范式。从理论上讲，为HMILL的关键组件的设计合理，我们将通用近似定理的扩展显示到框架中实现的模型所实现的所有功能的集合。本文还包含有关我们实施中技术和绩效改进的详细讨论，该讨论将在MIT许可下发布供下载。该框架的主要资产是其灵活性，它可以通过相同的工具对不同的现实世界数据源进行建模。除了单独观察到每个对象的一组属性的标准设置外，我们解释了如何在框架中实现表示整个对象系统的图表中的消息推断。为了支持我们的主张，我们使用框架解决了网络安全域的三个不同问题。第一种用例涉及来自原始网络观察结果的IoT设备识别。在第二个问题中，我们研究了如何使用以有向图表示的操作系统的快照可以对恶意二进制文件进行分类。最后提供的示例是通过网络中实体之间建模域黑名单扩展的任务。在所有三个问题中，基于建议的框架的解决方案可实现与专业方法相当的性能。

This work introduces a model that can recognize objects in images even if no training data is available for the object class. The only necessary knowledge about unseen visual categories comes from unsupervised text corpora. Unlike previous zero-shot learning models, which can only differentiate between unseen classes, our model can operate on a mixture of seen and unseen classes, simultaneously obtaining state of the art performance on classes with thousands of training images and reasonable performance on unseen classes. This is achieved by seeing the distributions of words in texts as a semantic space for understanding what objects look like. Our deep learning model does not require any manually defined semantic or visual features for either words or images. Images are mapped to be close to semantic word vectors corresponding to their classes, and the resulting image embeddings can be used to distinguish whether an image is of a seen or unseen class. We then use novelty detection methods to differentiate unseen classes from seen classes. We demonstrate two novelty detection strategies; the first gives high accuracy on unseen classes, while the second is conservative in its prediction of novelty and keeps the seen classes' accuracy high.

自我监督的视觉表示学习最近引起了重大的研究兴趣。虽然一种评估自我监督表示的常见方法是通过转移到各种下游任务，但我们研究了衡量其可解释性的问题，即了解原始表示中编码的语义。我们将后者提出为估计表示和手动标记概念空间之间的相互信息。为了量化这一点，我们介绍了一个解码瓶颈：必须通过简单的预测变量捕获信息，将概念映射到表示空间中的簇。我们称之为反向线性探测的方法为表示表示的语义敏感。该措施还能够检测出表示何时包含概念的组合（例如“红色苹果”），而不仅仅是单个属性（独立的“红色”和“苹果”）。最后，我们建议使用监督分类器自动标记大型数据集，以丰富用于探测的概念的空间。我们使用我们的方法来评估大量的自我监督表示形式，通过解释性对它们进行排名，并通过线性探针与标准评估相比出现的差异，并讨论了一些定性的见解。代码为：{\ Scriptsize {\ url {https://github.com/iro-cp/ssl-qrp}}}}}。

深度学习技术导致了通用对象检测领域的显着突破，近年来产生了很多场景理解的任务。由于其强大的语义表示和应用于场景理解，场景图一直是研究的焦点。场景图生成（SGG）是指自动将图像映射到语义结构场景图中的任务，这需要正确标记检测到的对象及其关系。虽然这是一项具有挑战性的任务，但社区已经提出了许多SGG方法并取得了良好的效果。在本文中，我们对深度学习技术带来了近期成就的全面调查。我们审查了138个代表作品，涵盖了不同的输入方式，并系统地将现有的基于图像的SGG方法从特征提取和融合的角度进行了综述。我们试图通过全面的方式对现有的视觉关系检测方法进行连接和系统化现有的视觉关系检测方法，概述和解释SGG的机制和策略。最后，我们通过深入讨论当前存在的问题和未来的研究方向来完成这项调查。本调查将帮助读者更好地了解当前的研究状况和想法。

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework. Specifically, we propose a weighted maximum margin framework for semantic manifold-based recognition that incorporates distance constraints from (both supervised and unsupervised) vocabulary atoms. Distance constraints ensure that labeled samples are projected closer to their correct prototypes, in the embedding space, than to others. We illustrate that resulting model shows improvements in supervised, zero-shot, generalized zero-shot, and large open set recognition, with up to 310K class vocabulary on Animal with Attributes and ImageNet datasets.

零拍分类问题的大多数现有算法通常依赖于类别之间基于属性的语义关系，以实现新型类别的分类而不观察其任何实例。但是，训练零拍分类模型仍然需要训练数据集中的每个类（甚至是实例）的属性标记，这也是昂贵的。为此，在本文中，我们提出了一个新的问题场景：“我们是否能够为新颖的属性探测器/分类器获得零射击学习，并使用它们自动注释数据集以进行标记效率？”基本上，仅给予一小组探测器，这些探测器都学会了识别一些手动注释的属性（即，所见属性），我们的目标是以零射学学习方式综合新颖属性的探测器。我们所提出的方法，零拍摄的属性（ZSLA），这是我们最好的知识中的第一个，通过应用SET操作首先将所看到的属性分解为基本属性，然后重新组合地解决这一新的研究问题。这些基本属性进入了新颖的属性。进行广泛的实验以验证我们合成探测器的能力，以便准确地捕获新颖性的语义，并与其他基线方法相比，在检测和定位方面表现出优越的性能。此外，在CALTECH-UCSD鸟类-200-2011 DataSet上使用仅32个属性，我们所提出的方法能够合成其他207个新颖的属性，而在由我们合成重新注释的数据集上培训的各种广义零拍分类算法属性探测器能够提供可比性的性能与手动地理注释有关的那些。

Scene categorization is a fundamental problem in computer vision. However, scene understanding research has been constrained by the limited scope of currently-used databases which do not capture the full variety of scene categories. Whereas standard databases for object categorization contain hundreds of different classes of objects, the largest available dataset of scene categories contains only 15 classes. In this paper we propose the extensive Scene UNderstanding (SUN) database that contains 899 categories and 130,519 images. We use 397 well-sampled categories to evaluate numerous state-of-the-art algorithms for scene recognition and establish new bounds of performance. We measure human scene classification performance on the SUN database and compare this with computational methods. Additionally, we study a finer-grained scene representation to detect scenes embedded inside of larger scenes.

The International Workshop on Reading Music Systems (WoRMS) is a workshop that tries to connect researchers who develop systems for reading music, such as in the field of Optical Music Recognition, with other researchers and practitioners that could benefit from such systems, like librarians or musicologists. The relevant topics of interest for the workshop include, but are not limited to: Music reading systems; Optical music recognition; Datasets and performance evaluation; Image processing on music scores; Writer identification; Authoring, editing, storing and presentation systems for music scores; Multi-modal systems; Novel input-methods for music to produce written music; Web-based Music Information Retrieval services; Applications and projects; Use-cases related to written music. These are the proceedings of the 3rd International Workshop on Reading Music Systems, held in Alicante on the 23rd of July 2021.

State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since additional labeled data is needed to specify any other visual concept. Learning directly from raw text about images is a promising alternative which leverages a much broader source of supervision. We demonstrate that the simple pre-training task of predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of 400 million (image, text) pairs collected from the internet. After pre-training, natural language is used to reference learned visual concepts (or describe new ones) enabling zero-shot transfer of the model to downstream tasks. We study the performance of this approach by benchmarking on over 30 different existing computer vision datasets, spanning tasks such as OCR, action recognition in videos, geo-localization, and many types of fine-grained object classification. The model transfers non-trivially to most tasks and is often competitive with a fully supervised baseline without the need for any dataset specific training. For instance, we match the accuracy of the original ResNet-50 on ImageNet zero-shot without needing to use any of the 1.28 million training examples it was trained on. We release our code and pre-trained model weights at https://github.com/OpenAI/CLIP.

Inspired by strategies like Active Learning, it is intuitive that intelligently selecting the training classes from a dataset for Zero-Shot Learning (ZSL) can improve the performance of existing ZSL methods. In this work, we propose a framework called Diverse and Rare Class Identifier (DiRaC-I) which, given an attribute-based dataset, can intelligently yield the most suitable "seen classes" for training ZSL models. DiRaC-I has two main goals - constructing a diversified set of seed classes, followed by a visual-semantic mining algorithm initialized by these seed classes that acquires the classes capturing both diversity and rarity in the object domain adequately. These classes can then be used as "seen classes" to train ZSL models for image classification. We adopt a real-world scenario where novel object classes are available to neither DiRaC-I nor the ZSL models during training and conducted extensive experiments on two benchmark data sets for zero-shot image classification - CUB and SUN. Our results demonstrate DiRaC-I helps ZSL models to achieve significant classification accuracy improvements.

The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, auto-encoders, manifold learning, and deep networks. This motivates longer-term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation and manifold learning.

细粒度的图像分析（FGIA）是计算机视觉和模式识别中的长期和基本问题，并为一组多种现实世界应用提供了基础。 FGIA的任务是从属类别分析视觉物体，例如汽车或汽车型号的种类。细粒度分析中固有的小阶级和阶级阶级内变异使其成为一个具有挑战性的问题。利用深度学习的进步，近年来，我们在深入学习动力的FGIA中见证了显着进展。在本文中，我们对这些进展的系统进行了系统的调查，我们试图通过巩固两个基本的细粒度研究领域 - 细粒度的图像识别和细粒度的图像检索来重新定义和扩大FGIA领域。此外，我们还审查了FGIA的其他关键问题，例如公开可用的基准数据集和相关域的特定于应用程序。我们通过突出几个研究方向和开放问题，从社区中突出了几个研究方向和开放问题。

这篇综述的目的是将读者介绍到图表内，以将其应用于化学信息学中的分类问题。图内核是使我们能够推断分子的化学特性的功能，可以帮助您完成诸如寻找适合药物设计的化合物等任务。内核方法的使用只是一种特殊的两种方式量化了图之间的相似性。我们将讨论限制在这种方法上，尽管近年来已经出现了流行的替代方法，但最著名的是图形神经网络。