Humans refer to objects in their environments all the time, especially in dialogue with other people. We explore generating and comprehending natural language referring expressions for objects in images.In particular, we focus on incorporating better measures of visual context into referring expression models and find that visual comparison to other objects within an image helps improve performance significantly. We also develop methods to tie the language generation process together, so that we generate expressions for all objects of a particular category jointly. Evaluation on three recent datasets -RefCOCO, RefCOCO+, and RefCOCOg 1 , shows the advantages of our methods for both referring expression generation and comprehension.

In this paper we introduce a new game to crowd-source natural language referring expressions. By designing a two player game, we can both collect and verify referring expressions directly within the game. To date, the game has produced a dataset containing 130,525 expressions, referring to 96,654 distinct objects, in 19,894 photographs of natural scenes. This dataset is larger and more varied than previous REG datasets and allows us to study referring expressions in real-world scenes. We provide an in depth analysis of the resulting dataset. Based on our findings, we design a new optimization based model for generating referring expressions and perform experimental evaluations on 3 test sets.

We propose a method that can generate an unambiguous description (known as a referring expression) of a specific object or region in an image, and which can also comprehend or interpret such an expression to infer which object is being described. We show that our method outperforms previous methods that generate descriptions of objects without taking into account other potentially ambiguous objects in the scene. Our model is inspired by recent successes of deep learning methods for image captioning, but while image captioning is difficult to evaluate, our task allows for easy objective evaluation. We also present a new large-scale dataset for referring expressions, based on MS-COCO. We have released the dataset and a toolbox for visualization and evaluation, see https://github.com/ mjhucla/Google_Refexp_toolbox.

In this paper, we address referring expression comprehension: localizing an image region described by a natural language expression. While most recent work treats expressions as a single unit, we propose to decompose them into three modular components related to subject appearance, location, and relationship to other objects. This allows us to flexibly adapt to expressions containing different types of information in an end-to-end framework. In our model, which we call the Modular Attention Network (MAttNet), two types of attention are utilized: languagebased attention that learns the module weights as well as the word/phrase attention that each module should focus on; and visual attention that allows the subject and relationship modules to focus on relevant image components. Module weights combine scores from all three modules dynamically to output an overall score. Experiments show that MAttNet outperforms previous state-of-the-art methods by a large margin on both bounding-box-level and pixel-level comprehension tasks. Demo 1 and code 2 are provided.

当前的自动驾驶汽车技术主要集中于将乘客从A点带到B。但是，已经证明乘客害怕乘坐自动驾驶汽车。减轻此问题的一种方法是允许乘客给汽车提供自然语言命令。但是，汽车可能会误解发布的命令或视觉环境，这可能导致不确定的情况。希望自动驾驶汽车检测到这些情况并与乘客互动以解决它们。本文提出了一个模型，该模型检测到命令时不确定的情况并找到引起该命令的视觉对象。可选地，包括描述不确定对象的系统生成的问题。我们认为，如果汽车可以以人类的方式解释这些物体，乘客就可以对汽车能力获得更多信心。因此，我们研究了如何（1）检测不确定的情况及其根本原因，以及（2）如何为乘客产生澄清的问题。在对Talk2CAR数据集进行评估时，我们表明所提出的模型\ acrfull {pipeline}，改善\ gls {m：模棱两可 - absolute-Increse}，与$ iou _ {.5} $相比，与不使用\ gls {pipeline {pipeline {pipeline { }。此外，我们设计了一个引用表达生成器（reg）\ acrfull {reg_model}量身定制的自动驾驶汽车设置，该设置可产生\ gls {m：流星伴侣} Meteor的相对改进，\ gls \ gls {m：rouge felative}}与最先进的REG模型相比，Rouge-L的速度快三倍。

弱监督的参考表达接地（REG）旨在将特定目标扎根于语言表达描述的图像中，同时缺乏目标和表达之间的对应关系。弱监督的REG存在两个主要问题。首先，缺乏区域级注释会引入建议和查询之间的歧义。其次，大多数以前的弱监督的REG方法忽略了指南的判别位置和上下文，从而在将目标与其他相同类别对象区分开时造成了困难。为了应对上述挑战，我们设计了实体增强的自适应重建网络（enail）。具体而言，赚取包括三个模块：实体增强，自适应接地和协作重建。在实体增强中，我们计算语义相似性作为监督选择候选建议。自适应接地可以在主题，位置和背景下以分层关注计算候选提案的排名评分。协作重建从三个角度衡量排名结果：自适应重建，语言重建和属性分类。自适应机制有助于减轻不同参考表达式的差异。五个数据集的实验表明，赚取胜于现有的最新方法。定性结果表明，提议的收入可以更好地处理特定类别的多个对象在一起的情况。

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

The Flickr30k dataset has become a standard benchmark for sentence-based image description. This paper presents Flickr30k Entities, which augments the 158k captions from Flickr30k with 244k coreference chains, linking mentions of the same entities across different captions for the same image, and associating them with 276k manually annotated bounding boxes. Such annotations are essential for continued progress in automatic image description and grounded language understanding. They enable us to define a new benchmark for localization of textual entity mentions in an image. We present a strong baseline for this task that combines an image-text embedding, detectors for common objects, a color classifier, and a bias towards selecting larger objects. While our baseline rivals in accuracy more complex state-of-the-art models, we show that its gains cannot be easily parlayed into improvements on such tasks as image-sentence retrieval, thus underlining the limitations of current methods and the need for further research.

人群的理解由于其重要的实际意义引起了人们对视觉领域的普遍兴趣。不幸的是，没有努力探索桥接自然语言和计算机视觉的多模式领域中的人群理解。参考表达理解（REF）是一项代表性的多模式任务。当前的REF研究更多地集中在一般情况下从多个独特类别中扎根目标对象。很难应用于复杂的现实世界人群的理解。为了填补这一空白，我们提出了一个新的挑战性数据集，称为Refcrowd，该数据集旨在通过参考表达方式寻找人群中的目标人。它不仅需要充分挖掘自然语言信息，而且还需要仔细地专注于目标与具有相似外观的人群之间的细微差异，以实现从语言到视觉的细粒度映射。此外，我们提出了一个细粒度的多模式属性对比网络（FMAC），以在人群的理解中处理参考。它首先将复杂的视觉和语言特征分解为属性感知的多模式特征，然后捕获歧视性但健壮性的细粒属性特征，以有效地区分相似人之间的这些细微差异。所提出的方法优于我们的档案数据集和现有参考数据集中的现有最新方法（SOTA）方法。此外，我们为多模式域中的更深入研究实施了端到端的REF工具箱。我们的数据集和代码可以在：\ url {https://qiuheqian.github.io/datasets/refcrowd/}中可用。

对象接地任务旨在通过口头通信定位图像中的目标对象。了解人类命令是有效人体机器人通信所需的重要过程。然而，这是具有挑战性的，因为人类命令可能是暧昧和错误的。本文旨在消除人类的引用表达式，允许代理基于从场景图获得的语义数据提出相关问题。我们测试如果我们的代理可以从场景图之间使用对象之间的关系，以便询问可以消除原始用户命令的语义相关问题。在本文中，我们使用场景图（IGSG）提出增量接地，消歧模型使用从图像场景图和语言场景图到基于人类命令的地面对象的语义数据的歧义模型。与基线相比，IGSG显示了有希望的成果，在有多个相同的目标对象的复杂现实场景中。 IGSG可以通过要求消除歧义问题回到用户来有效消除歧义或错误的表达式。

We present a model that generates natural language descriptions of images and their regions. Our approach leverages datasets of images and their sentence descriptions to learn about the inter-modal correspondences between language and visual data. Our alignment model is based on a novel combination of Convolutional Neural Networks over image regions, bidirectional Recurrent Neural Networks over sentences, and a structured objective that aligns the two modalities through a multimodal embedding. We then describe a Multimodal Recurrent Neural Network architecture that uses the inferred alignments to learn to generate novel descriptions of image regions. We demonstrate that our alignment model produces state of the art results in retrieval experiments on Flickr8K, Flickr30K and MSCOCO datasets. We then show that the generated descriptions significantly outperform retrieval baselines on both full images and on a new dataset of region-level annotations.

图像字幕显示可以通过使用场景图来表示图像中对象的关系来实现更好的性能。当前字幕编码器通常使用图形卷积网（GCN）来表示关系信息，并通过串联或卷积将其与对象区域特征合并，以获取句子解码的最终输入。但是，由于两个原因，现有方法中基于GCN的编码器在字幕上的有效性较小。首先，使用图像字幕作为目标（即最大似然估计），而不是以关系为中心的损失无法完全探索编码器的潜力。其次，使用预训练的模型代替编码器本身提取关系不是灵活的，并且不能有助于模型的解释性。为了提高图像字幕的质量，我们提出了一个新颖的体系结构改革者 - 一种关系变压器，可以生成具有嵌入关系信息的功能，并明确表达图像中对象之间的成对关系。改革者将场景图的生成目标与使用一个修改后的变压器模型的图像字幕结合在一起。这种设计使改革者不仅可以通过提取强大的关系图像特征的利益生成更好的图像标题，还可以生成场景图，以明确描述配对关系。公开可用数据集的实验表明，我们的模型在图像字幕和场景图生成上的最先进方法明显优于最先进的方法

We have seen great progress in basic perceptual tasks such as object recognition and detection. However, AI models still fail to match humans in high-level vision tasks due to the lack of capacities for deeper reasoning. Recently the new task of visual question answering (QA) has been proposed to evaluate a model's capacity for deep image understanding. Previous works have established a loose, global association between QA sentences and images. However, many questions and answers, in practice, relate to local regions in the images. We establish a semantic link between textual descriptions and image regions by object-level grounding. It enables a new type of QA with visual answers, in addition to textual answers used in previous work. We study the visual QA tasks in a grounded setting with a large collection of 7W multiple-choice QA pairs. Furthermore, we evaluate human performance and several baseline models on the QA tasks. Finally, we propose a novel LSTM model with spatial attention to tackle the 7W QA tasks.

We develop and demonstrate automatic image description methods using a large captioned photo collection. One contribution is our technique for the automatic collection of this new dataset -performing a huge number of Flickr queries and then filtering the noisy results down to 1 million images with associated visually relevant captions. Such a collection allows us to approach the extremely challenging problem of description generation using relatively simple non-parametric methods and produces surprisingly effective results. We also develop methods incorporating many state of the art, but fairly noisy, estimates of image content to produce even more pleasing results. Finally we introduce a new objective performance measure for image captioning.

Visual understanding goes well beyond object recognition. With one glance at an image, we can effortlessly imagine the world beyond the pixels: for instance, we can infer people's actions, goals, and mental states. While this task is easy for humans, it is tremendously difficult for today's vision systems, requiring higher-order cognition and commonsense reasoning about the world. We formalize this task as Visual Commonsense Reasoning. Given a challenging question about an image, a machine must answer correctly and then provide a rationale justifying its answer.Next, we introduce a new dataset, VCR, consisting of 290k multiple choice QA problems derived from 110k movie scenes. The key recipe for generating non-trivial and highquality problems at scale is Adversarial Matching, a new approach to transform rich annotations into multiple choice questions with minimal bias. Experimental results show that while humans find VCR easy (over 90% accuracy), state-of-the-art vision models struggle (∼45%).To move towards cognition-level understanding, we present a new reasoning engine, Recognition to Cognition Networks (R2C), that models the necessary layered inferences for grounding, contextualization, and reasoning. R2C helps narrow the gap between humans and machines (∼65%); still, the challenge is far from solved, and we provide analysis that suggests avenues for future work.

基于文本的视频细分旨在通过用文本查询指定演员及其表演动作来细分视频序列中的演员。由于\ emph {emph {语义不对称}的问题，以前的方法无法根据演员及其动作以细粒度的方式将视频内容与文本查询对齐。 \ emph {语义不对称}意味着在多模式融合过程中包含不同量的语义信息。为了减轻这个问题，我们提出了一个新颖的演员和动作模块化网络，该网络将演员及其动作分别定位在两个单独的模块中。具体来说，我们首先从视频和文本查询中学习与参与者相关的内容，然后以对称方式匹配它们以定位目标管。目标管包含所需的参与者和动作，然后将其送入完全卷积的网络，以预测演员的分割掩模。我们的方法还建立了对象的关联，使其与所提出的时间建议聚合机制交叉多个框架。这使我们的方法能够有效地细分视频并保持预测的时间一致性。整个模型允许联合学习参与者的匹配和细分，并在A2D句子和J-HMDB句子数据集上实现单帧细分和完整视频细分的最新性能。

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-

参考表达理解（REC）是视觉推理中最重要的任务之一，该任务需要模型来检测自然语言表达式所引用的目标对象。在拟议的管道中，一阶段参考表达理解（OSREC）已成为主要趋势，因为它合并了区域建议和选择阶段。许多最先进的OSREC模型采用多跳的推理策略，因为单个表达式中经常提到一系列对象，该对象需要多跳的推理来分析语义关系。但是，这些模型的一个未解决的问题是，在推理之前需要预先定义和固定推理步骤的数量，而忽略了表达式的不同复杂性。在本文中，我们提出了一个动态的多步推理网络，该网络允许根据推理状态和表达复杂性动态调整推理步骤。具体而言，我们采用变压器模块来记住和处理推理状态和增强学习策略，以动态推断推理步骤。这项工作可在几个REC数据集上实现最新性能或重大改进，从具有短表达式的Refcoco（+，G）到具有长而复杂的组成表达式的数据集。

如何最好地将涉及语言和视觉的多模式任务中的语言和感知处理整合在一起是一个重要的开放问题。在这项工作中，我们认为，以自上而下的方式使用语言的常见做法，直接通过高级视觉特征进行视觉关注，这可能不是最佳的。我们假设使用语言还可以调节从像素到高级功能的自下而上处理可以为整体性能带来好处。为了支持我们的主张，我们提出了一个基于U-NET的模型，并对两个语言视觉密集预测任务进行实验：引用表达式分割和语言引导的图像着色。我们比较结果，其中一个或自下而上的视觉分支都以语言为条件。我们的实验表明，除了自上而下的注意力外，使用语言来控制自下而上的视觉处理，从而为任务和实现竞争性表现提供更好的结果。我们的语言分析表明，自下而上的条件改善对象的细分，尤其是当输入文本是指低级视觉概念时。代码可在https://github.com/ilkerkesen/bvpr上找到。

Automatically describing the content of an image is a fundamental problem in artificial intelligence that connects computer vision and natural language processing. In this paper, we present a generative model based on a deep recurrent architecture that combines recent advances in computer vision and machine translation and that can be used to generate natural sentences describing an image. The model is trained to maximize the likelihood of the target description sentence given the training image. Experiments on several datasets show the accuracy of the model and the fluency of the language it learns solely from image descriptions. Our model is often quite accurate, which we verify both qualitatively and quantitatively. For instance, while the current state-of-the-art BLEU-1 score (the higher the better) on the Pascal dataset is 25, our approach yields 59, to be compared to human performance around 69. We also show BLEU-1 score improvements on Flickr30k, from 56 to 66, and on SBU, from 19 to 28. Lastly, on the newly released COCO dataset, we achieve a BLEU-4 of 27.7, which is the current state-of-the-art.