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

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.

This paper presents a unified Vision-Language Pre-training (VLP) model. The model is unified in that (1) it can be finetuned for either vision-language generation (e.g., image captioning) or understanding (e.g., visual question answering) tasks, and (2) it uses a shared multi-layer transformer network for both encoding and decoding, which differs from many existing methods where the encoder and decoder are implemented using separate models. The unified VLP model is pre-trained on a large amount of image-text pairs using the unsupervised learning objectives of two tasks: bidirectional and sequence-to-sequence (seq2seq) masked vision-language prediction. The two tasks differ solely in what context the prediction conditions on. This is controlled by utilizing specific self-attention masks for the shared transformer network. To the best of our knowledge, VLP is the first reported model that achieves state-of-the-art results on both vision-language generation and understanding tasks, as disparate as image captioning and visual question answering, across three challenging benchmark datasets: COCO Captions, Flickr30k Captions, and VQA 2.0. The code and the pre-trained models are available at https://github.com/LuoweiZhou/VLP.

Joint image-text embedding is the bedrock for most Visionand-Language (V+L) tasks, where multimodality inputs are simultaneously processed for joint visual and textual understanding. In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings. We design four pre-training tasks: Masked Language Modeling (MLM), Masked Region Modeling (MRM, with three variants), Image-Text Matching (ITM), and Word-Region Alignment (WRA). Different from previous work that applies joint random masking to both modalities, we use conditional masking on pre-training tasks (i.e., masked language/region modeling is conditioned on full observation of image/text). In addition to ITM for global image-text alignment, we also propose WRA via the use of Optimal Transport (OT) to explicitly encourage finegrained alignment between words and image regions during pre-training. Comprehensive analysis shows that both conditional masking and OTbased WRA contribute to better pre-training. We also conduct a thorough ablation study to find an optimal combination of pre-training tasks. Extensive experiments show that UNITER achieves new state of the art across six V+L tasks (over nine datasets), including Visual Question

以前的视觉语言预训练模型主要构建具有令牌和对象（像素）的多模式输入，然后在它们之间执行交叉模式相互作用。我们认为，只有令牌和对象的输入限制了诸如短语到区域接地之类的高级语义对齐。同时，多层次对齐本质上是一致的，并且能够协同促进表示形式学习。因此，在本文中，我们建议学习视觉预训练（MVPTR）的多级语义一致性。在MVPTR中，我们遵循两种方式的嵌套结构，以引入概念为高级语义。为了简化从多模式多级输入的学习，我们的框架分为两个阶段，第一阶段着重于模式内多级表示学习，第二阶段通过粗粒和细粒度跨模态强化了跨模式的交互语义对齐任务。除了常用的图像文本匹配和掩盖语言模型任务外，我们还引入了第一阶段蒙版概念恢复任务以增强概念表示学习，第二阶段的另外两个任务在第二阶段中，以明确鼓励跨跨层次的多层次对准方式。我们的代码可在https://github.com/junction4nako/mvp_pytorch上找到。

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。

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。

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.

远见和语言预测已成为解决多模式下游任务的普遍方法。当前的趋势是朝着更大的模型和预处理数据集迈进。从长远来看，这一计算头急促似乎是不合理的，而是朝着可持续的解决方案迈进，事实上，排除了资源有限的学术实验室。在这项工作中，我们提出了一个称为VICHA的新框架，该框架有效利用输入数据以通过以下方式提高学习，以： ，（c）利用图像级注释，称为视觉概念，使用现有基础模型（例如剪辑）获得，以提高图像编码器的性能。尽管对数据的预估计少了四倍，但我们的VICHA策略在下游任务（例如图像文本检索，VQA，视觉推理，视觉上和视觉接地）上的其他方法优于其他方法。该代码将在此处公开提供：https：//github.com/mshukor/vicha

We present ViLBERT (short for Vision-and-Language BERT), a model for learning task-agnostic joint representations of image content and natural language. We extend the popular BERT architecture to a multi-modal two-stream model, processing both visual and textual inputs in separate streams that interact through co-attentional transformer layers. We pretrain our model through two proxy tasks on the large, automatically collected Conceptual Captions dataset and then transfer it to multiple established vision-and-language tasks -visual question answering, visual commonsense reasoning, referring expressions, and caption-based image retrieval -by making only minor additions to the base architecture. We observe significant improvements across tasks compared to existing task-specific modelsachieving state-of-the-art on all four tasks. Our work represents a shift away from learning groundings between vision and language only as part of task training and towards treating visual grounding as a pretrainable and transferable capability.Preprint. Under review.

我们提出了GLIPV2，这是一个接地的VL理解模型，该模型既服务于本地化任务（例如，对象检测，实例分割）和视觉语言（VL）理解任务（例如VQA，图像字幕）。 GLIPV2优雅地将本地化预训练和视觉语言预训练（VLP）具有三个预训练任务：短语接地作为对检测任务的VL重新重新制定，区域词对比度学习作为新型的区域词对比度对比度对比学习任务，以及蒙面的语言建模。这种统一不仅简化了先前的多阶段VLP程序，而且还可以在本地化和理解任务之间实现相互利益。实验结果表明，在各种本地化和理解任务上，单个GLIPV2模型（所有模型权重）在SOTA性能附近实现。该模型还显示了（1）在开放式摄制对象检测任务上进行的强零射击和很少的自适应性能，以及（2）VL理解任务上的卓越接地能力。代码将在https://github.com/microsoft/glip上发布。

视觉问题应答（VQA）任务利用视觉图像和语言分析来回回答图像的文本问题。它是一个流行的研究课题，在过去十年中越来越多的现实应用。本文介绍了我们最近对AliceMind-MMU的研究（阿里巴巴的编码器 - 解码器来自Damo Academy - 多媒体理解的机器智能实验室），其比人类在VQA上获得相似甚至略微更好的结果。这是通过系统地改善VQA流水线来实现的，包括：（1）具有全面的视觉和文本特征表示的预培训; （2）与学习参加的有效跨模型互动; （3）一个新颖的知识挖掘框架，具有专门的专业专家模块，适用于复杂的VQA任务。处理不同类型的视觉问题，需要具有相应的专业知识在提高我们的VQA架构的表现方面发挥着重要作用，这取决于人力水平。进行了广泛的实验和分析，以证明新的研究工作的有效性。

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。

Vision-Language预培训是一个新兴和快速发展的研究主题，将多模态知识从丰富的资源预训练任务转移到有限资源下游任务。与主要学习单个通用编码器的现有作品不同，我们提出了一种可训练的通用编码器 - 解码器网络（UNI-EDEN），以促进视觉语言感知（例如，视觉问题应答）和生成（例如，图像标题）。 UNI-EDEN是一种基于双流变换器的结构，由三个模块组成：对象和句子编码器，其单独了解每个模态的表示，以及通过模态交互能够实现多模态推理和句子的句子解码器。考虑到每个图像的语言表示可以跨越该层次结构的不同粒度，包括从简单到全面，个人标签，短语和自然句子，我们通过多粒愿景语言代理任务预先列车UNI-EDEN：屏蔽对象分类（MOC），蒙版区域短语生成（MRPG），图像句匹配（ISM）和屏蔽句生成（MSG）。以这种方式，UNI-EDEN赋予了多模态表示提取和语言建模的功率。广泛的实验证明了通过微调到四个视觉语言感知和发电下游任务来展示Uni-Eden的概括性。

The availability of large-scale image captioning and visual question answering datasets has contributed significantly to recent successes in vision-and-language pretraining. However, these datasets are often collected with overrestrictive requirements inherited from their original target tasks (e.g., image caption generation), which limit the resulting dataset scale and diversity. We take a step further in pushing the limits of vision-and-language pretraining data by relaxing the data collection pipeline used in Conceptual Captions 3M (CC3M) [70] and introduce the Conceptual 12M (CC12M), a dataset with 12 million image-text pairs specifically meant to be used for visionand-language pre-training. We perform an analysis of this dataset and benchmark its effectiveness against CC3M on multiple downstream tasks with an emphasis on long-tail visual recognition. Our results clearly illustrate the benefit of scaling up pre-training data for vision-and-language tasks, as indicated by the new state-of-the-art results on both the nocaps and Conceptual Captions benchmarks. 1

Learning fine-grained interplay between vision and language allows to a more accurate understanding for VisionLanguage tasks. However, it remains challenging to extract key image regions according to the texts for semantic alignments. Most existing works are either limited by textagnostic and redundant regions obtained with the frozen detectors, or failing to scale further due to its heavy reliance on scarce grounding (gold) data to pre-train detectors. To solve these problems, we propose Self-Locator Aided Network (SLAN) for cross-modal understanding tasks without any extra gold data. SLAN consists of a region filter and a region adaptor to localize regions of interest conditioned on different texts. By aggregating cross-modal information, the region filter selects key regions and the region adaptor updates their coordinates with text guidance. With detailed region-word alignments, SLAN can be easily generalized to many downstream tasks. It achieves fairly competitive results on five cross-modal understanding tasks (e.g., 85.7% and 69.2% on COCO image-to-text and text-to-image retrieval, surpassing previous SOTA methods). SLAN also demonstrates strong zero-shot and fine-tuned transferability to two localization tasks.

本文介绍了Omnivl，这是一种新的基础模型，旨在使用一种通用体系结构来支持图像语言和视频语言任务。它为图像和视频输入采用了统一的基于变压器的视觉编码器，因此可以执行联合图像语言和视频语言预处理。我们首次证明了这样的范式受益于图像和视频任务，而不是传统的单向传输（例如，使用图像语言来帮助视频语言）。为此，我们提出了对图像语言和视频语言的脱钩关节预处理，以有效地将视觉模型分解为空间和时间维度，并在图像和视频任务上获得性能提升。此外，我们引入了一种新颖的统一视觉对比度（UNIVLC）损失，以利用图像文本，视频文本，图像标签（例如，图像分类），视频标签（例如，视频动作识别）在一起受到监督和吵闹的监督预处理数据都尽可能多地利用。无需额外的任务适配器，Omnivl可以同时支持仅视觉任务（例如，图像分类，视频操作识别），跨模式对齐任务（例如，图像/视频 - 文本检索）和多模式理解和生成任务（例如，图像/视频问答，字幕）。我们在各种下游任务上评估Omnivl，并以相似的模型大小和数据量表获得最新的或竞争结果。

最近，跨模式的预训练任务一直是一个热点，因为它在各种下文研究中广泛应用，包括检索，字幕，问题答案等。然而，退出的方法采用单媒体预训练模型来探索进行跨模式检索的联合视觉表示，这很容易遭受计算爆炸的影响。此外，尽管常规的双流结构非常有效，但它们仍然缺乏重要的跨模式相互作用，导致性能低。在这些挑战的激励下，我们提出了一个对比的跨模式知识共享预训练（Cookie），以掌握联合文本图像表示。从结构上讲，Cookie由于可接受的时间消耗而采用了传统的双流结构。为了克服上述双流结构的固有缺陷，我们精心设计了两个有效的模块。具体而言，第一个模块是一个体重共享的变压器，它构建在视觉和文本编码器的头上，旨在将语义对齐文本和图像对齐。该设计使视觉和文本路径集中在相同的语义上。另一个是三个专门设计的对比学习，旨在分享不同模型之间的知识。共享的跨模式知识大大发展了单峰表示的研究，从而促进了单模式检索任务。对多模式匹配研究的广泛实验结果，包括跨模式检索，文本匹配和图像检索揭示了我们的计算效率和我们预训练模型的统计指标的上级。

Vision语言中最现有的方法依赖于通过对象检测提取的对象中心特征，并在提取的功能和文本之间进行细粒度对齐。我们认为物体检测的使用可能不适合视觉语言预培训。相反，我们指出应该执行任务，以便文本中提到的“视觉概念”的区域位于图像中，并且在文本和视觉概念之间的平时对齐中，识别在其中的校准处于多个 - 粒度。本文提出了一种称为X-VLM的新方法，以执行“多粒度的视觉语言预训练”。实验结果表明，X-VLM在许多下游视觉语言任务中始终如一地优于最先进的方法。

Large-scale vision and language representation learning has shown promising improvements on various vision-language tasks. Most existing methods employ a transformer-based multimodal encoder to jointly model visual tokens (region-based image features) and word tokens. Because the visual tokens and word tokens are unaligned, it is challenging for the multimodal encoder to learn image-text interactions. In this paper, we introduce a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. Unlike most existing methods, our method does not require bounding box annotations nor high-resolution images. To improve learning from noisy web data, we propose momentum distillation, a self-training method which learns from pseudo-targets produced by a momentum model. We provide a theoretical analysis of ALBEF from a mutual information maximization perspective, showing that different training tasks can be interpreted as different ways to generate views for an image-text pair. ALBEF achieves state-of-the-art performance on multiple downstream visionlanguage tasks. On image-text retrieval, ALBEF outperforms methods that are pre-trained on orders of magnitude larger datasets. On VQA and NLVR 2 , ALBEF achieves absolute improvements of 2.37% and 3.84% compared to the state-ofthe-art, while enjoying faster inference speed. Code and models are available at https://github.com/salesforce/ALBEF.