自从出现以来，在大型，随机收集的数据上训练的视觉模型在许多领域都有重大影响。但是，由于它们在各个领域表现出色，例如图像文本 - 取回，因此他们的内部工作仍未得到充分了解。当前的工作分析了这些模型的真实零击功能。我们从分析培训语料库的分析开始，评估测试类的程度（以及哪个）实际上是零射击，以及与单个类别的性能如何相关。我们跟进这些模型的基于属性的零击学习能力的分析，以评估这种经典的零击概念从大规模的监督中出现的方式。我们利用最近发布的LAION400M数据语料库以及公开可用的剪辑，OpenClip和Flava的模型，评估了基于属性的CUB和AWA2基准的零摄影功能。我们的分析表明：（i）在预训练期间（很多）观察到大多数流行的零射门基准中的大多数类别； （ii）零射击性能主要来自模型识别类标签的能力，每当它们存在于文本中时，并且只有在不使用类标签时才能观察到基于属性的zeroshot学习的较低的性能能力； （iii）所使用的属性数量可能会对性能产生重大影响，并且很容易导致大幅下降。

We investigate how well CLIP understands texture in natural images described by natural language. To this end, we analyze CLIP's ability to: (1) perform zero-shot learning on various texture and material classification datasets; (2) represent compositional properties of texture such as red dots or yellow stripes on the Describable Texture in Detail(DTDD) dataset; and (3) aid fine-grained categorization of birds in photographs described by color and texture of their body parts.

Contrastive Language-Image Pre-training (CLIP) has emerged as a simple yet effective way to train large-scale vision-language models. CLIP demonstrates impressive zero-shot classification and retrieval on diverse downstream tasks. However, to leverage its full potential, fine-tuning still appears to be necessary. Fine-tuning the entire CLIP model can be resource-intensive and unstable. Moreover, recent methods that aim to circumvent this need for fine-tuning still require access to images from the target distribution. In this paper, we pursue a different approach and explore the regime of training-free "name-only transfer" in which the only knowledge we possess about the downstream task comprises the names of downstream target categories. We propose a novel method, SuS-X, consisting of two key building blocks -- SuS and TIP-X, that requires neither intensive fine-tuning nor costly labelled data. SuS-X achieves state-of-the-art zero-shot classification results on 19 benchmark datasets. We further show the utility of TIP-X in the training-free few-shot setting, where we again achieve state-of-the-art results over strong training-free baselines. Code is available at https://github.com/vishaal27/SuS-X.

我们引入了构图软提示（CSP），这是一种参数有效的学习技术，可改善大规模预处理视觉模型（VLMS）的零摄像组成性。 VLM可以在其灵活的文本编码器中代表任意类作为自然语言提示，但在组成零击基准任务上的表现不佳。为了改善VLM，我们提出了一种新颖的软提示形式。我们将构成的属性和对象视为将类定义为词汇的可学习令牌，并在多个及时的构图上调整它们。在推断期间，我们在新组合中重新组装了学习的属性对象词汇。我们表明，CSP在基准数据集上的原始VLM的表现平均为AUC上的10.9个百分点。 CSP还胜过Coop，这是一种调谐前缀上下文的软提示方法，在AUC上平均要点5.8个百分点。我们执行其他实验，以表明CSP对仅属性分类，高阶属性 - 属性对象组成以及预验证属性和微调对象的组合进行了改进。

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.

Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance. In this paper, we investigate whether computer vision models can also provide correct rationales for their predictions. We propose a ``doubly right'' object recognition benchmark, where the metric requires the model to simultaneously produce both the right labels as well as the right rationales. We find that state-of-the-art visual models, such as CLIP, often provide incorrect rationales for their categorical predictions. However, by transferring the rationales from language models into visual representations through a tailored dataset, we show that we can learn a ``why prompt,'' which adapts large visual representations to produce correct rationales. Visualizations and empirical experiments show that our prompts significantly improve performance on doubly right object recognition, in addition to zero-shot transfer to unseen tasks and datasets.

当前机器学习的大部分基础的大型数据集提出了有关不适当内容的严重问题，例如冒犯，侮辱，威胁或可能引起焦虑。这要求增加数据集文档，例如使用数据表。它们除其他主题外，还鼓励反思数据集的组成。到目前为止，该文档是手动完成的，因此可能是乏味且容易出错的，尤其是对于大型图像数据集。在这里，我们询问了机器是否可以帮助我们反思不适当的内容的“循环”问题，回答了数据表中的问题16。为此，我们建议使用存储在预训练的变压器模型中的信息来协助我们进行文档过程。具体而言，基于社会 - 道德价值数据集的及时调整引导剪辑以识别潜在的不适当的内容，从而减少了人工的劳动。然后，我们根据使用视觉模型生成的字幕来记录使用单词云找到的不适当图像。两个流行的大规模计算机视觉数据集的文档（ImageNet和OpenImages）以这种方式产生，这表明机器确实可以帮助数据集创建者回答有关不适当图像内容的问题16。

本文提出了一种对比调整，这是一种简单的方法，采用对比训练来对准图像和文本模型，同时仍然利用他们的预训练。在我们的实证研究中，我们发现，锁定的预训练图像模型与解锁文本模型最佳。我们调用这种对比调整“锁定图像文本调整”（LIT TOONING）的实例，该实例仅教导文本模型，从预先训练的图像模型中读出了良好的表示新任务。亮度调谐模型将零拍摄传输到新视觉任务的能力提高，例如图像分类或检索。建议的亮度调整是广泛适用的;它可以使用三种不同的图像文本数据集可靠地使用多种预训练方法（监督和无监督）和多种架构（Reset，Vision变换器和MLP-MILLER）。利用基于变压器的预训练VIT-G / 14型号，LIT调谐模型在想象网测试集中实现了84.5％的零射频传输精度，并且在充满挑战的分发ObjectNet测试集中实现了81.1％。

Large-scale vision-language models such as CLIP have shown impressive performance on zero-shot image classification and image-to-text retrieval. However, such zero-shot performance of CLIP-based models does not realize in tasks that require a finer-grained correspondence between vision and language, such as Visual Question Answering (VQA). We investigate why this is the case, and report an interesting phenomenon of CLIP, which we call the Concept Association Bias (CAB), as a potential cause of the difficulty of applying CLIP to VQA and similar tasks. CAB is especially apparent when two concepts are present in the given image while a text prompt only contains a single concept. In such a case, we find that CLIP tends to treat input as a bag of concepts and attempts to fill in the other missing concept crossmodally, leading to an unexpected zero-shot prediction. For example, when asked for the color of a lemon in an image, CLIP predicts ``purple'' if the image contains a lemon and an eggplant. We demonstrate the Concept Association Bias of CLIP by showing that CLIP's zero-shot classification performance greatly suffers when there is a strong concept association between an object (e.g. lemon) and an attribute (e.g. its color). On the other hand, when the association between object and attribute is weak, we do not see this phenomenon. Furthermore, we show that CAB is significantly mitigated when we enable CLIP to learn deeper structure across image and text embeddings by adding an additional Transformer on top of CLIP and fine-tuning it on VQA. We find that across such fine-tuned variants of CLIP, the strength of CAB in a model predicts how well it performs on VQA.

传统的计算机视觉模型受过培训，以预测固定的预定义类别。最近，自然语言已被证明是一个更广泛而更丰富的监督来源，为视觉概念提供更精细的描述，而不是监督“黄金”标签。以前的作品，例如剪辑，使用InfoNce丢失来训练模型以预测图像和文本标题之间的配对。然而，剪辑是饥饿的数据，需要超过400米的图像文本对进行培训。效率低下可以归因于图像文本对嘈杂的事实。为了解决这个问题，我们提出了水獭（有效的零射击识别的最佳运输蒸馏），它使用在线熵最佳运输，找到一个软图像文本与标签进行对比学习。基于预磨料的图像和文本编码器，用电站培训的型号实现了强大的性能，只有3M图像文本对。与InfoNce损失相比，标记平滑和知识蒸馏，OTTER始终如一地优于零拍摄图像（19,958类）和来自腾讯ML图像的多标记Imagenet 10k（10032类）的零拍摄评估中的这些基线。在4个不同的数据集/架构设置x 6度量上，OTTER优于（32）或绑定（2）34中的所有基准。

Incidental supervision from language has become a popular approach for learning generic visual representations that can be prompted to perform many recognition tasks in computer vision. We conduct an in-depth exploration of the CLIP model and show that its visual representation is often strongly biased towards solving some tasks more than others. Moreover, which task the representation will be biased towards is unpredictable, with little consistency across images. To resolve this task bias, we show how to learn a visual prompt that guides the representation towards features relevant to their task of interest. Our results show that these visual prompts can be independent of the input image and still effectively provide a conditioning mechanism to steer visual representations towards the desired task.

最近的文本到图像匹配模型对大型图像和句子的大公司进行了对比学习。虽然这些模型可以提供用于匹配和随后的零拍任务的强大分数，但它们不能给出给定图像的标题。在这项工作中，我们重新利用这些模型来生成在推理时间的图像时生成描述性文本，而无需进一步的训练或调整步骤。这是通过将具有大语言模型的视觉语义模型组合，从两种网络级模型中的知识中获益。由受监督标题方法获得的标题的限制性较小。此外，作为零射击学习方法，它非常灵活，我们展示了执行图像算法的能力，其中输入可以是图像或文本，输出是句子。这使得新颖的高级视觉能力，例如比较两个图像或解决视觉类比测试。

诸如剪辑之类的大型预训练的视觉模型在学习表现方面表现出巨大的潜力，这些模型可以在各种下游任务中转移。与主要基于离散标签的传统表示学习不同，视觉语言预训练会使图像和文本在公共特征空间中对齐，这允许通过提示零弹性转移到下游任务，即从分类权重合成。描述兴趣类的自然语言。在这项工作中，我们表明，在实践中部署此类模型的一个重大挑战是及时的工程，它需要域专业知识，并且非常耗时 - 由于措辞的略有变化，需要花费大量时间来进行单词调整可能会对性能产生巨大影响。受到自然语言处理（NLP）迅速学习研究的最新进展的启发，我们提出了上下文优化（COP），这是一种专门用于调整类似剪辑的视觉语言模型的简单方法，用于下游图像识别。具体而言，Coop用可学习的向量建模了提示A的上下文单词，而整个预训练的参数则保持固定。为了处理不同的图像识别任务，我们提供了两个COOP的实现：统一上下文和特定于班级的上下文。通过在11个数据集上进行的大量实验，我们证明Coop只需要一两个镜头才能以相当的利润击败手工制作的提示，并且能够以16张镜头（例如16张照片）获得迅速工程的显着改进增益约为15％（最高达到45％以上）。尽管是一种基于学习的方法，但与使用手工制作的提示相比，Coop与零拍模型相比，取得了出色的域泛化性能。

Prompt tuning is a new few-shot transfer learning technique that only tunes the learnable prompt for pre-trained vision and language models such as CLIP. However, existing prompt tuning methods tend to learn spurious or entangled representations, which leads to poor generalization to unseen concepts. Towards non-spurious and efficient prompt learning from limited examples, this paper presents a novel \underline{\textbf{C}}ounterfactual \underline{\textbf{P}}rompt \underline{\textbf{L}}earning (CPL) method for vision and language models, which simultaneously employs counterfactual generation and contrastive learning in a joint optimization framework. Particularly, CPL constructs counterfactual by identifying minimal non-spurious feature change between semantically-similar positive and negative samples that causes concept change, and learns more generalizable prompt representation from both factual and counterfactual examples via contrastive learning. Extensive experiments demonstrate that CPL can obtain superior few-shot performance on different vision and language tasks than previous prompt tuning methods on CLIP. On image classification, we achieve 3.55\% average relative improvement on unseen classes across seven datasets; on image-text retrieval and visual question answering, we gain up to 4.09\% and 25.08\% relative improvements across three few-shot scenarios on unseen test sets respectively.

现实世界的识别系统在实践中经常遇到许多看不见的标签。为了识别这种看不见的标签，多标签的零光学习（ML-ZSL）着重于通过预先训练的文本标签嵌入（例如，手套）传输知识。但是，这种方法仅利用语言模型利用单极知识，同时忽略了图像文本对固有的丰富语义信息。取而代之的是，最近开发的基于开放式摄影的方法（OV）方法成功地利用了对象检测中图像文本对的此类信息，并实现了令人印象深刻的性能。受基于OV的方法的成功启发，我们提出了一个新型的开放式视频框架，称为多模式知识转移（MKT），用于多标签分类。具体而言，我们的方法利用基于视觉和语言预处理（VLP）模型的图像文本对的多模式知识。为了促进VLP模型的Imagetext匹配能力，使用知识蒸馏来保证图像和标签嵌入的一致性以及及时调整以进一步更新标签嵌入。为了进一步识别多个对象，开发了一个简单但有效的两流模块，以捕获本地和全局功能。广泛的实验结果表明，我们的方法在公共基准数据集上的表现明显优于最先进的方法。代码将在https://github.com/seanhe97/mkt上找到。

Vision-language models (VLMs) such as CLIP have shown promising performance on a variety of recognition tasks using the standard zero-shot classification procedure -- computing similarity between the query image and the embedded words for each category. By only using the category name, they neglect to make use of the rich context of additional information that language affords. The procedure gives no intermediate understanding of why a category is chosen, and furthermore provides no mechanism for adjusting the criteria used towards this decision. We present an alternative framework for classification with VLMs, which we call classification by description. We ask VLMs to check for descriptive features rather than broad categories: to find a tiger, look for its stripes; its claws; and more. By basing decisions on these descriptors, we can provide additional cues that encourage using the features we want to be used. In the process, we can get a clear idea of what features the model uses to construct its decision; it gains some level of inherent explainability. We query large language models (e.g., GPT-3) for these descriptors to obtain them in a scalable way. Extensive experiments show our framework has numerous advantages past interpretability. We show improvements in accuracy on ImageNet across distribution shifts; demonstrate the ability to adapt VLMs to recognize concepts unseen during training; and illustrate how descriptors can be edited to effectively mitigate bias compared to the baseline.

Prompt Tuning, conditioning on task-specific learned prompt vectors, has emerged as a data-efficient and parameter-efficient method for adapting large pretrained vision-language models to multiple downstream tasks. However, existing approaches usually consider learning prompt vectors for each task independently from scratch, thereby failing to exploit the rich shareable knowledge across different vision-language tasks. In this paper, we propose multitask vision-language prompt tuning (MVLPT), which incorporates cross-task knowledge into prompt tuning for vision-language models. Specifically, (i) we demonstrate the effectiveness of learning a single transferable prompt from multiple source tasks to initialize the prompt for each target task; (ii) we show many target tasks can benefit each other from sharing prompt vectors and thus can be jointly learned via multitask prompt tuning. We benchmark the proposed MVLPT using three representative prompt tuning methods, namely text prompt tuning, visual prompt tuning, and the unified vision-language prompt tuning. Results in 20 vision tasks demonstrate that the proposed approach outperforms all single-task baseline prompt tuning methods, setting the new state-of-the-art on the few-shot ELEVATER benchmarks and cross-task generalization benchmarks. To understand where the cross-task knowledge is most effective, we also conduct a large-scale study on task transferability with 20 vision tasks in 400 combinations for each prompt tuning method. It shows that the most performant MVLPT for each prompt tuning method prefers different task combinations and many tasks can benefit each other, depending on their visual similarity and label similarity. Code is available at https://github.com/sIncerass/MVLPT.

我们大多数人不是特定领域的专家，例如鸟类学。尽管如此，我们确实有一般的图像和语言理解，我们用来匹配我们所看到的专家资源。这使我们能够扩展我们的知识并在没有临时外部监督的情况下执行新的任务。相反，除非培训专门考虑到​​这一知识，否则机器更加难以咨询专家策划知识库。因此，在本文中，我们考虑了一个新问题：没有专家注释的细粒度的图像识别，我们通过利用Web百科全书中提供的广泛知识来解决这些问题。首先，我们学习模型来描述使用非专家图像描述来描述对象的视觉外观。然后，我们培训一个细粒度的文本相似性模型，它与句子级别的文件描述匹配。我们在两个数据集上评估该方法，并与跨模型检索的几个强大的基线和最先进的技术相比。代码可用：https://github.com/subhc/clever

Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited settings. Nonetheless, visual data (e.g., images) is by default prerequisite for learning prompts in existing methods. In this work, we advocate that the effectiveness of image-text contrastive learning in aligning the two modalities (for training CLIP) further makes it feasible to treat texts as images for prompt tuning and introduce TaI prompting. In contrast to the visual data, text descriptions are easy to collect, and their class labels can be directly derived. Particularly, we apply TaI prompting to multi-label image recognition, where sentences in the wild serve as alternatives to images for prompt tuning. Moreover, with TaI, double-grained prompt tuning (TaI-DPT) is further presented to extract both coarse-grained and fine-grained embeddings for enhancing the multi-label recognition performance. Experimental results show that our proposed TaI-DPT outperforms zero-shot CLIP by a large margin on multiple benchmarks, e.g., MS-COCO, VOC2007, and NUS-WIDE, while it can be combined with existing methods of prompting from images to improve recognition performance further. Code is released at https://github.com/guozix/TaI-DPT.

视觉语言预训练（VLP）模型在各种下游任务上表现出色。他们的成功在很大程度上取决于预训练的跨模式数据集的规模。但是，中文中缺乏大规模数据集和基准阻碍了中国VLP模型和更广泛的多语言应用程序的发展。在这项工作中，我们发布了一个名为Wukong的大型中国跨模式数据集，其中包含从网络收集的1亿个中文图像文本对。 Wukong旨在基准基准不同的多模式预训练方法，以促进VLP研究和社区发展。此外，我们发布了一组模型，预先训练了各种图像编码器（vit-b/vit-l/swint），还将高级预训练技术应用于VLP，例如锁定图像文本调整，相对于代币的相似性学习和减少互动。还提供了广泛的实验和不同下游任务的基准测试，包括新的最大人验证的图像文本测试数据集。实验表明，Wukong可以作为不同的跨模式学习方法的有前途的中国预培训数据集和基准。对于10个数据集上的零摄像图像分类任务，$ Wukong_ {vit-l} $达到的平均准确度为73.03％。对于图像文本检索任务，它在AIC-ICC上的平均召回率为71.6％，比Wenlan 2.0高12.9％。此外，我们的Wukong模型在下游任务上进行了基准测试，例如多个数据集上的其他变体，例如Flickr8k-CN，Flickr-30K-CN，Coco-CN，Coco-CN等。更多信息可以参考：https：//wukong-dataset.github.io/wukong-dataset/。