2017 VisualGenomeConnectingLanguagea

(Krishna et al., 2017) ⇒ Ranjay Krishna, Yuke Zhu, Oliver Groth, Justin Johnson, Kenji H_ata, Joshua Kravitz, Stephanie Chen, Yannis Kalantidis, Li-Jia Li, David A. Shamma, Michael S. Bernstein, and Li Fei-Fei. (2017). “Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations.” In: International Journal of Computer Vision, 123(1). doi:10.1007/s11263-016-0981-7

Subject Headings: Visual Genome Dataset, Vision Task.

Notes

Cited By

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Abstract

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 an image. When asked "What vehicle is the person riding? ", computers will need to identify the objects in an image as well as the relationships riding (man, carriage) and pulling (horse, carriage) to answer correctly that "the person is riding a horse-drawn carriage." In this paper, we present the Visual Genome dataset to enable the modeling of such relationships. We collect dense annotations of objects, attributes, and relationships within each image to learn these models. Specifically, our dataset contains over 108K images where each image has an average of [math]\displaystyle{ 35 }[/math] objects, [math]\displaystyle{ 26 }[/math] attributes, and [math]\displaystyle{ 21 }[/math] pairwise relationships between objects. We canonicalize the objects, attributes, relationships, and noun phrases in region descriptions and questions answer pairs to WordNet synsets. Together, these annotations represent the densest and largest dataset of image descriptions, objects, attributes, relationships, and question answer pairs.

1. Introduction

Fig. 1 An overview of the data needed to move from perceptual awareness to cognitive understanding of images. We present a dataset of images densely annotated with numerous region descriptions, objects, attributes, and relationships. Some examples of region descriptions (e.g. “girl feeding large elephant” and “a man taking a picture behind girl”) are shown (top). The objects (e.g. elephant), attributes (e.g. large) and relationships (e.g. feeding) are shown (bottom). Our dataset also contains image related question answer pairs (not shown)

A holy grail of computer vision is the complete understanding of visual scenes: a model that is able to name and detect objects, describe their attributes, and recognize their relationships. Understanding scenes would enable important applications such as image search, question answering, and robotic interactions. Much progress has been made in recent years towards this goal, including image classification (Perronnin et al. 2010; Simonyan and Zisserman 2014; Krizhevsky et al. 2012; Szegedy et al. 2015) and object detection (Girshick et al. 2014; Sermanet et al. 2013; Girshick 2015; Ren et al. 2015b). An important contributing factor is the availability of a large amount of data that drives the statistical models that underpin today’s advances in computational visual understanding. While the progress is exciting, we are still far from reaching the goal of comprehensive scene understanding. As Fig. 1 shows, existing models would be able to detect discrete objects in a photo but would not be able to explain their interactions or the relationships between them. Such explanations tend to be cognitive in nature, integrating perceptual information into conclusions about the relationships between objects in a scene (Bruner 1990; Firestone and Scholl 2015). A cognitive understanding of our visual world thus requires that we complement computers’ ability to detect objects with abilities to describe those objects (Isola et al. 2015) and understand their interactions within a scene (Sadeghi and Farhadi 2011).

There is an increasing effort to put together the next generation of datasets to serve as training and benchmarking datasets for these deeper, cognitive scene understanding and reasoning tasks, the most notable being MS-COCO (Lin et al. 2014) and VQA (Antol et al. 2015). The MS-COCO dataset consists of 300K real-world photos collected from Flickr. For each image, there is pixel-level segmentation of 80 object classes (when present) and 5 independent, user-generated sentences describing the scene. VQA adds to this a set of 614K question answer pairs related to the visual contents of each image (see more details in Sect. 3.1). With this information, MS-COCO and VQA provide a fertile training and testing ground for models aimed at tasks for accurate object detection, segmentation, and summary-level image captioning (Kiros et al. 2014; Mao et al. 2014; Karpathy and Fei-Fei 2015) as well as basic QA (Ren et al. 2015a; Malinowski et al. 2015; Gao et al. 2015; Malinowski and Fritz 2014). For example, a state-of-the-art model (Karpathy and Fei-Fei 2015) provides a description of one MS-COCO image in Fig. 1 as “two men are standing next to an elephant.” But what is missing is the further understanding of where each object is, what each person is doing, what the relationship between the person and elephant is, etc. Without such relationships, these models fail to differentiate this image from other images of people next to elephants.

To understand images thoroughly, we believe three key elements need to be added to existing datasets: a grounding of visual concepts to language (Kiros et al. 2014), a more complete set of descriptions and QAs for each image based on multiple image regions (Johnson et al. 2015), and a formalized representation of the components of an image (Hayes 1978). In the spirit of mapping out this complete information of the visual world, we introduce the Visual Genome dataset. The first release of the Visual Genome dataset uses 108,077

images from the intersection of the YFCC100M (Thomee et al. 2016) and MS-COCO (Lin et al. 2014). Section 5 provides a more detailed description of the dataset. We highlight below the motivation and contributions of the three key elements that set Visual Genome apart from existing datasets.

The Visual Genome dataset regards relationships and attributes as first-class citizens of the annotation space, in addition to the traditional focus on objects. Recognition of relationships and attributes is an important part of the complete understanding of the visual scene, and in many cases, these elements are key to the story of a scene (e.g., the difference between “a dog chasing a man” versus “a man chasing a dog”). The Visual Genome dataset is among the first to provide a detailed labeling of object interactions and attributes, grounding visual concepts to language.1

An image is often a rich scenery that cannot be fully described in one summarizing sentence. The scene in Fig. 1 contains multiple “stories”: “a man taking a photo of elephants,” “a woman feeding an elephant,” “a river in the background of lush grounds,” etc. Existing datasets such as Flickr 30K (Young et al. 2014) and MS-COCO (Lin et al. 2014) focus on high-level descriptions of an image.2 Instead, for each image in the Visual Genome dataset, we collect more than 50

descriptions for different regions in the image, providing a much denser and more complete set of descriptions of the scene. In addition, inspired by VQA (Antol et al. 2015), we also collect an average of 17 question answer pairs based on the descriptions for each image. Region-based question answers can be used to jointly develop NLP and vision models that can answer questions from either the description or the image, or both of them.

With a set of dense descriptions of an image and the explicit correspondences between visual pixels (i.e. bounding boxes of objects) and textual descriptors (i.e. relationships, attributes), the Visual Genome dataset is poised to be the first image dataset that is capable of providing a structured formalized representation of an image, in the form that is widely used in knowledge base representations in NLP (Zhou et al. 2007; GuoDong et al. 2005; Culotta and Sorensen 2004; Socher et al. 2012). For example, in Fig. 1, we can formally express the relationship holding between the woman and food as holding(woman, food). Putting together all the objects and relations in a scene, we can represent each image as a scene graph (Johnson et al. 2015). The scene graph representation has been shown to improve semantic image retrieval (Johnson et al. 2015; Schuster et al. 2015) and image captioning (Farhadi et al. 2009; Chang et al. 2014; Gupta and Davis 2008). Furthermore, all objects, attributes and relationships in each image in the Visual Genome dataset are canonicalized to its corresponding WordNet (Miller 1995) ID (called a synset ID). This mapping connects all images in Visual Genome and provides an effective way to consistently query the same concept (object, attribute, or relationship) in the dataset. It can also potentially help train models that can learn from contextual information from multiple images (Figs. 2, 3).

In this paper, we introduce the Visual Genome dataset with the aim of training and benchmarking the next generation of computer models for comprehensive scene understanding. The paper proceeds as follows: In Sect. 2, we provide a detailed description of each component of the dataset. Section 3 provides a literature review of related datasets as well as related recognition tasks. Section 4 discusses the crowdsourcing strategies we deployed in the ongoing effort of collecting this dataset. Section 5 is a collection of data analysis statistics, showcasing the key properties of the Visual Genome dataset. Last but not least, Sect. 6 provides a set of experimental results that use Visual Genome as a benchmark. Further visualizations, API, and additional information on the Visual Genome dataset can be found online.3 Open image in new windowFig. 2 Fig. 2

An example image from the Visual Genome dataset. We show 3 region descriptions and their corresponding region graphs. We also show the connected scene graph collected by combining all of the image’s region graphs. The top region description is “a man and a woman sit on a park bench along a river.” It contains the objects: man, woman, bench and river. The relationships that connect these objects are: sits_on(man, bench), in_front_of(man, river), and sits_on(woman, bench) Open image in new windowFig. 3 Fig. 3

An example image from our dataset along with its scene graph representation. The scene graph contains objects (child, instructor, helmet, etc.) that are localized in the image as bounding boxes (not shown). These objects also have attributes: large, green, behind, etc. Finally, objects are connected to each other through relationships: wears(child, helmet), wears(instructor, jacket), etc Open image in new windowFig. 4 Fig. 4

A representation of the Visual Genome dataset. Each image contains region descriptions that describe a localized portion of the image. We collect two types of question answer pairs (QAs): freeform QAs and region-based QAs. Each region is converted to a region graph representation of objects, attributes, and pairwise relationships. Finally, each of these region graphs are combined to form a scene graph with all the objects grounded to the image. Best viewed in color 2 Visual Genome Data Representation

The Visual Genome dataset consists of seven main components: region descriptions, objects, attributes, relationships, region graphs, scene graphs, and question answer pairs. Figure 4 shows examples of each component for one image. To enable research on comprehensive understanding of images, we begin by collecting descriptions and question answers. These are raw texts without any restrictions on length or vocabulary. Next, we extract objects, attributes and relationships from our descriptions. Together, objects, attributes and relationships comprise our scene graphs that represent a formal representation of an image. In this section, we break down Fig. 4 and explain each of the seven components. In Sect. 4, we will describe in more detail how data from each component is collected through a crowdsourcing platform.

2.1 Multiple Regions and Their Descriptions

…

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2017 VisualGenomeConnectingLanguagea	Li Fei-Fei Ranjay Krishna Yuke Zhu Oliver Groth Justin Johnson Joshua Kravitz Stephanie Chen Yannis Kalantidis Li-Jia Li David A. Shamma Michael S. Bernstein Kenji H ata			Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations				10.1007/s11263-016-0981-7		2017