Recent years have witnessed the rapid progress of image captioning. However, the demands for large memory storage and heavy computational burden prevent these captioning models from being deployed on mobile devices. The main obstacles lie in the heavyweight visual feature extractors (i.e., object detectors) and complicated cross-modal fusion networks. To this end, we propose LightCap, a lightweight image captioner for resource-limited devices. The core design is built on the recent CLIP model for efficient image captioning. To be specific, on the one hand, we leverage the CLIP model to extract the compact grid features without relying on the time-consuming object detectors. On the other hand, we transfer the image-text retrieval design of CLIP to image captioning scenarios by devising a novel visual concept extractor and a cross-modal modulator. We further optimize the cross-modal fusion model and parallel prediction heads via sequential and ensemble distillations. With the carefully designed architecture, our model merely contains 40M parameters, saving the model size by more than 75% and the FLOPs by more than 98% in comparison with the current state-of-the-art methods. In spite of the low capacity, our model still exhibits state-of-the-art performance on prevalent datasets, e.g., 136.6 CIDEr on COCO Karpathy test split. Testing on the smartphone with only a single CPU, the proposed LightCap exhibits a fast inference speed of 188ms per image, which is ready for practical applications.

Despite the remarkable progress of image captioning, existing captioners typically lack the controllable capability to generate desired image captions, e.g., describing the image in a rough or detailed manner, in a factual or emotional view, etc. In this paper, we show that a unified model is qualified to perform well in diverse domains and freely switch among multiple styles. Such a controllable capability is achieved by embedding the prompt learning into the image captioning framework. To be specific, we design a set of prompts to fine-tune the pre-trained image captioner. These prompts allow the model to absorb stylized data from different domains for joint training, without performance degradation in each domain. Furthermore, we optimize the prompts with learnable vectors in the continuous word embedding space, avoiding the heuristic prompt engineering and meanwhile exhibiting superior performance. In the inference stage, our model is able to generate desired stylized captions by choosing the corresponding prompts. Extensive experiments verify the controllable capability of the proposed method. Notably, we achieve outstanding performance on two diverse image captioning benchmarks including COCO Karpathy split and TextCaps using a unified model.

近年来在开发更好的图像标题模型方面取得了巨大进展，但其中大多数依赖于单独的对象探测器来提取区域特征。最近的视觉语言研究通过利用网格表示来实现更灵活的模型训练和更快推理速度的速度来转向探测器趋势。但是，这种发展主要专注于图像理解任务，并且对标题生成任务的研究仍然较少。在本文中，我们涉及一种更好的无需探测器图像标题模型，并提出了一种基于纯视觉变压器的图像标题模型，称为VITCAP，其中使用了网格表示而不提取区域特征。为了提高性能，我们介绍了一种新颖的概念令牌网络（CTN）来预测语义概念，然后将它们纳入端到端的标题。特别地，CTN是基于视觉变换器构建的，并且旨在通过分类任务预测概念令牌，其中包含丰富的语义信息极大地利益标题任务。与以前的探测器的模型相比，Vitcap大大简化了架构，同时在各种具有挑战性的图像标题数据集上实现了竞争性能。特别是，Vitcap分别达到138.1苹果酒分数，即在Nocaps上的Coco-Caption Karpatal-Splity，93.8和108.6苹果酒分数和Google-CC标题数据集上分别达到138.1苹果酒分数。

我们提出了一种跨模型关注蒸馏框架，用于培训双编码器模型，用于了解视觉语言理解任务，例如视觉推理和视觉问题应答。双编码器模型的推理速度比Fusion-encoder模型更快，并在推理期间启用图像和文本的预算。然而，双编码器模型中使用的浅交互模块不足以处理复杂的视觉语言理解任务。为了学习图像和文本的深度互动，我们引入了跨模型注意蒸馏，它使用融合编码器模型的图像到文本和文本到图像注意力分布来指导我们的双编码器的培训模型。此外，我们表明，适用于预训练和微调阶段的跨模型注意蒸馏实现了进一步的改进。实验结果表明，蒸馏的双编码器模型可实现视觉推理，视觉征求和视觉问题的竞争性能，同时享受比Fusion-Conoder模型更快的推理速度。我们的代码和型号将在https://github.com/kugwzk/distilled -dualiCoder上公开提供。

自我监督的视觉和语言预处理（VLP）旨在从大规模的图像文本数据中学习可转移的多模式表示形式，并在填充后在广泛的视觉范围内实现强大的表现。以前的主流VLP方法通常采用依靠外部对象检测器来编码多模式变压器框架中的图像的两步策略，该框架遭受了限制性对象概念空间，有限的图像上下文和效率低下的计算。在本文中，我们提出了一个对象感知的端到端VLP框架，该框架将来自CNN的图像网格特征直接馈送到变压器中，并共同学习多模式表示。更重要的是，我们建议执行对象知识蒸馏，以促进在不同语义级别的学习跨模式对齐。为了实现这一目标，我们通过将对象特征及其来自外部检测器的语义标签作为监督来设计两个新颖的借口任务：1。）对象引导的蒙版视觉建模任务的重点是在多模式变压器中强制执行对象感知的表示的学习； 2.）短语区域对准任务旨在通过利用语言空间中名词短语和对象标签之间的相似性来改善跨模式对齐。对各种视觉语言任务进行的广泛实验证明了我们提出的框架的功效，并且我们在现有的预科策略中实现了竞争性或优越的表现。

现有视觉语言预训练（VLP）方法主要依赖于配对的图像文本数据集，这些数据集由大量人类劳动注释，或者从互联网上爬行，然后是精心制作的数据清洁技术。为了减少对良好的图像文本对的依赖，有望直接利用仅大规模的仅文本和仅图像的语料库。本文提出了一种数据增强方法，即跨模式cutmix（CMC），用于在未配对的VLP中进行隐式跨模式对齐学习。具体而言，CMC将自然句子从文本视图转换为多模式视图，在该视图中，句子中的视觉词语单词被带有相似语义的各种图像贴片随机替换。拟议中的CMC有几个吸引人的礼节。首先，它增强了数据多样性，同时保持语义含义完好无损地解决了对齐数据稀缺的问题；其次，通过将跨模式噪声连接到单模式数据上，它指导模型以学习跨模态的令牌级相互作用，以更好地降级。此外，我们提出了一种名为VLMIXER的新的未配对VLP方法，该方法将CMC与对比度学习集成在一起，以将Uni-Mododal和多模式视图汇总在一起，以在不同模式之间进行更好的实例级别对齐。在五个下游任务上进行的广泛实验表明，VLMIXER可以超过以前最先进的未配对VLP方法。

Large-scale pre-training methods of learning cross-modal representations on image-text pairs are becoming popular for vision-language tasks. While existing methods simply concatenate image region features and text features as input to the model to be pre-trained and use selfattention to learn image-text semantic alignments in a brute force manner, in this paper, we propose a new learning method Oscar 1 , which uses object tags detected in images as anchor points to significantly ease the learning of alignments. Our method is motivated by the observation that the salient objects in an image can be accurately detected, and are often mentioned in the paired text. We pre-train an Oscar model on the public corpus of 6.5 million text-image pairs, and fine-tune it on downstream tasks, creating new state-of-the-arts on six well-established vision-language understanding and generation tasks. 2

随着变压器的发展，近年来预先训练的模型已经以突破性的步伐发展。他们在自然语言处理（NLP）和计算机视觉（CV）中主导了主流技术。如何将预训练适应视觉和语言（V-L）学习和改善下游任务绩效成为多模式学习的重点。在本文中，我们回顾了视力语言预训练模型（VL-PTMS）的最新进展。作为核心内容，我们首先简要介绍了几种方法，将原始图像和文本编码为单模式嵌入在预训练之前。然后，我们在建模文本和图像表示之间的相互作用时深入研究VL-PTM的主流体系结构。我们进一步提出了广泛使用的预训练任务，然后我们介绍了一些常见的下游任务。我们终于结束了本文，并提出了一些有前途的研究方向。我们的调查旨在为研究人员提供合成和指向相关研究的指针。

Large-scale cross-modal pre-training paradigms have recently shown ubiquitous success on a wide range of downstream tasks, e.g., zero-shot classification, retrieval and image captioning. However, their successes highly rely on the scale and quality of web-crawled data that naturally contain incomplete and noisy information (e.g., wrong or irrelevant content). Existing works either design manual rules to clean data or generate pseudo-targets as auxiliary signals for reducing noise impact, which do not explicitly tackle both the incorrect and incomplete challenges simultaneously. In this paper, to automatically mitigate the impact of noise by solely mining over existing data, we propose a principled Noise-robust Language-Image Pre-training framework (NLIP) to stabilize pre-training via two schemes: noise-harmonization and noise-completion. First, in noise-harmonization scheme, NLIP estimates the noise probability of each pair according to the memorization effect of cross-modal transformers, then adopts noise-adaptive regularization to harmonize the cross-modal alignments with varying degrees. Second, in noise-completion scheme, to enrich the missing object information of text, NLIP injects a concept-conditioned cross-modal decoder to obtain semantic-consistent synthetic captions to complete noisy ones, which uses the retrieved visual concepts (i.e., objects' names) for the corresponding image to guide captioning generation. By collaboratively optimizing noise-harmonization and noise-completion schemes, our NLIP can alleviate the common noise effects during image-text pre-training in a more efficient way. Extensive experiments show the significant performance improvements of our NLIP using only 26M data over existing pre-trained models (e.g., CLIP, FILIP and BLIP) on 12 zero-shot classification datasets, MSCOCO image captioning and zero-shot image-text retrieval tasks.

随着图像文本对的大量数据以及视觉和语言（V＆L）任务的多样性，学者在该研究领域引入了大量的深度学习模型。此外，近年来，转移学习还显示出在计算机愿景中的巨大成功，例如图像分类，对象检测等以及在自然语言处理中以进行问答，机器翻译等的自然语言处理。继承转移学习的精神， V＆L的研究工作已经在大规模数据集上设计了多种预训练技术，以增强下游任务的性能。本文的目的是提供当代V＆L预审前模型的全面修订。特别是，我们对预处理的方法进行了分类和描述，以及最先进的视觉和语言预训练模型的摘要。此外，还提供了培训数据集和下游任务的列表，以进一步提高V＆L预处理的观点。最后，我们决定采取进一步的一步，讨论众多未来研究的方向。

变压器架构已经带来了计算语言领域的根本变化，这已经由经常性神经网络主导多年。它的成功还意味着具有语言和愿景的跨模型任务的大幅度变化，许多研究人员已经解决了这个问题。在本文中，我们审查了该领域中的一些最关键的里程碑，以及变压器架构如何纳入Visuol语言跨模型任务的整体趋势。此外，我们讨论了当前的局限性，并推测了我们发现迫在眉睫的一些前景。

This paper presents a detailed study of improving visual representations for vision language (VL) tasks and develops an improved object detection model to provide object-centric representations of images. Compared to the most widely used bottom-up and top-down model [2], the new model is bigger, better-designed for VL tasks, and pre-trained on much larger training corpora that combine multiple public annotated object detection datasets. Therefore, it can generate representations of a richer collection of visual objects and concepts. While previous VL research focuses mainly on improving the vision-language fusion model and leaves the object detection model improvement untouched, we show that visual features matter significantly in VL models. In our experiments we feed the visual features generated by the new object detection model into a Transformer-based VL fusion model OSCAR [21], and utilize an improved approach OSCAR+ to pre-train the VL model and fine-tune it on a wide range of downstream VL tasks. Our results show that the new visual features significantly improve the performance across all VL tasks, creating new state-of-the-art results on seven public benchmarks. Code, models and pre-extracted features are released at https://github.com/pzzhang/VinVL. ♥ Microsoft Corporation♠ University of Washington † indicates equal contributions.

在本文中，我们设计和训练生成的图像到文本变压器Git，以统一视觉语言任务，例如图像/视频字幕和问题答案。尽管生成模型在预训练和微调之间提供了一致的网络体系结构，但现有工作通常包含复杂的结构（Uni/多模式编码器/解码器），并取决于外部模块，例如对象检测器/标记器和光学角色识别（OCR） ）。在git中，我们将体系结构简化为一个图像编码器，而在单语言建模任务下将架构简化为一个文本解码器。我们还扩展了预训练数据和模型大小，以提高模型性能。没有铃铛和哨子，我们的git在12个具有挑战性的基准下建立了新的艺术状态。例如，我们的模型在文本贴图上首次超过了人类的表现（138.2 vs. 125.5在苹果酒中）。此外，我们提出了一种新的基于一代的图像分类和场景文本识别的方案，在标准基准上实现了不错的表现。

We study joint learning of Convolutional Neural Network (CNN) and Transformer for vision-language pre-training (VLPT) which aims to learn cross-modal alignments from millions of image-text pairs. State-of-the-art approaches extract salient image regions and align regions with words step-by-step. As region-based visual features usually represent parts of an image, it is challenging for existing visionlanguage models to fully understand the semantics from paired natural languages. In this paper, we propose SOHO to "See Out of tHe bOx" that takes a whole image as input, and learns vision-language representation in an endto-end manner. SOHO does not require bounding box annotations which enables inference 10 times faster than regionbased approaches. In particular, SOHO learns to extract comprehensive yet compact image features through a visual dictionary (VD) that facilitates cross-modal understanding. VD is designed to represent consistent visual abstractions of similar semantics. It is updated on-the-fly and utilized in our proposed pre-training task Masked Visual Modeling (MVM). We conduct experiments on four well-established vision-language tasks by following standard VLPT settings. In particular, SOHO achieves absolute gains of 2.0% R@1 score on MSCOCO text retrieval 5k test split, 1.5% accuracy on NLVR 2 test-P split, 6.7% accuracy on SNLI-VE test split, respectively.

连接视觉和语言在生成智能中起着重要作用。因此，已经致力于图像标题的大型研究工作，即用句法和语义有意义的句子描述图像。从2015年开始，该任务通常通过由Visual Encoder组成的管道和文本生成的语言模型来解决任务。在这些年来，两种组件通过对象区域，属性，介绍多模态连接，完全关注方法和伯特早期融合策略的利用而显着发展。但是，无论令人印象深刻的结果，图像标题的研究还没有达到结论性答案。这项工作旨在提供图像标题方法的全面概述，从视觉编码和文本生成到培训策略，数据集和评估度量。在这方面，我们量化地比较了许多相关的最先进的方法来确定架构和培训策略中最有影响力的技术创新。此外，讨论了问题的许多变体及其开放挑战。这项工作的最终目标是作为理解现有文献的工具，并突出显示计算机视觉和自然语言处理的研究领域的未来方向可以找到最佳的协同作用。

在本文中，我们提出了一种单一统一的变压器（UFO），其能够处理视觉语言的单峰输入（例如，图像或语言）或多模式输入（例如，图像和问题的串联）（ VL）表示学习。现有方法通常为每个模态和/或特定融合网络设计个人网络，用于多模式任务。为了简化网络架构，我们使用单个变压器网络并在VL预培训期间强制执行多任务学习，其包括图像文本对比丢失，图像文本匹配丢失和基于双向的屏蔽语言建模损耗SEQ2Seq注意面具。相同的变压器网络用作不同预训练任务中的图像编码器，文本编码器或融合网络。经验上，我们观察不同任务之间的冲突，并在视觉问题应答，Coco图像标题（交叉熵优化）和Nocaps（在香料中）实现新的艺术状态。在其他下游任务中，例如，图像文本检索，我们也实现了竞争性能。

Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-andlanguage downstream tasks. Current approaches to VLP heavily rely on image feature extraction processes, most of which involve region supervision (e.g., object detection) and the convolutional architecture (e.g., ResNet). Although disregarded in the literature, we find it problematic in terms of both (1) efficiency/speed, that simply extracting input features requires much more computation than the multimodal interaction steps; and (2) expressive power, as it is upper bounded to the expressive power of the visual embedder and its predefined visual vocabulary. In this paper, we present a minimal VLP model, Vision-and-Language Transformer (ViLT), monolithic in the sense that the processing of visual inputs is drastically simplified to just the same convolution-free manner that we process textual inputs. We show that ViLT is up to tens of times faster than previous VLP models, yet with competitive or better downstream task performance. Our code and pre-trained weights are available at https://github.com/dandelin/vilt.

近年来，具有两个较高架构的视觉语言（VL）模型主导了视觉表示的学习。当前的VL模型要么使用轻型Uni-Modal编码器，并在交叉模式编码器中同时提取，对齐和融合这两种模态，或者将最后一层的Uni-Modal-Modal特征直接馈入顶部的交叉模式编码器，而忽略了语义深度单模式编码器中不同级别的信息。两种方法都可能限制视觉表示学习和限制模型性能。在本文中，我们介绍了多个桥梁层，该层在Uni-Modal编码器的顶层和跨模式编码器的每一层之间建立了连接。这可以在不同语义级别的视觉和文本表示之间进行全面的自下而上相互作用，从而导致更有效的跨模式对齐和融合。我们提出的桥梁可以预先训练，仅需$ 4 $ m的图像，可以在各种下游视觉语言任务上实现最先进的性能。在VQAV2 Test-STD集合中，Bridge-Tower的准确性为$ 78.73 \％$，与以前的最先进的仪表型号相同的the Art仪表均优于先前的最先进的仪表\％$ $，并且几乎没有其他参数，并且几乎没有其他参数和其他参数计算成本。值得注意的是，当进一步扩展模型时，桥梁可以达到81.15美元\％$的准确性，超过了在较大的数据集中预先训练的模型。代码可在https://github.com/microsoft/bridgetower上找到。

使用图像文本对的对比语言图像预测（剪辑）在零拍摄和传输学习设置中的图像分类中取得了令人印象深刻的结果。但是，我们表明，直接应用此类模型以识别对象检测的图像区域导致由于域移位导致的性能差：剪辑训练以与文本描述的整体匹配，而不捕获图像之间的细粒度对齐地区和文本跨度。为了缓解此问题，我们提出了一种称为RegionClip的新方法，可显着扩展剪辑以学习区域级视觉表示，从而在图像区域和文本概念之间实现细粒度对齐。我们的方法利用剪辑模型将图像区域与模板标题匹配，然后预先列出我们的模型以对准要素空间中的这些区域文本对。将预磨料模型转移到开放词汇对象检测任务时，我们的方法显着优于3.8 AP50和2.2 AP的最新技术，分别用于COCO和LVIS数据集的新型类别。更多，学习区域表示支持对象检测的零拍摄推断，显示了对COCO和LVIS数据集的有希望的结果。我们的代码可在https://github.com/microsoft/regionclip上获得。

Vision-Language Transformers can be learned without human labels (e.g. class labels, bounding boxes, etc). Existing work, whether explicitly utilizing bounding boxes or patches, assumes that the visual backbone must first be trained on ImageNet class prediction before being integrated into a multimodal linguistic pipeline. We show that this is not necessary and introduce a new model Vision-Language from Captions (VLC) built on top of Masked Auto-Encoders that does not require this supervision. In fact, in a head-to-head comparison between ViLT, the current state-of-the-art patch-based vision-language transformer which is pretrained with supervised object classification, and our model, VLC, we find that our approach 1. outperforms ViLT on standard benchmarks, 2. provides more interpretable and intuitive patch visualizations, and 3. is competitive with many larger models that utilize ROIs trained on annotated bounding-boxes.