Open Images V6 — Now Featuring Localized Narratives
Posted by Jordi Pont-Tuset, Research Scientist, Google Research
Open Images is the largest annotated image dataset in many regards, for use in training the latest deep convolutional neural networks for computer vision tasks. With the introduction of version 5 last May, the Open Images dataset includes 9M images annotated with 36M image-level labels, 15.8M bounding boxes, 2.8M instance segmentations, and 391k visual relationships. Along with the dataset itself, the associated Open Images Challenges have spurred the latest advances in object detection, instance segmentation, and visual relationship detection.
Today, we are happy to announce the release of Open Images V6, which greatly expands the annotation of the Open Images dataset with a large set of new visual relationships (e.g., “dog catching a flying disk”), human action annotations (e.g., “woman jumping”), and image-level labels (e.g., “paisley”). Notably, this release also adds localized narratives, a completely new form of multimodal annotations that consist of synchronized voice, text, and mouse traces over the objects being described. In Open Images V6, these localized narratives are available for 500k of its images. Additionally, in order to facilitate comparison to previous works, we also release localized narratives annotations for the full 123k images of the COCO dataset.
Localized Narratives
One of the motivations behind localized narratives is to study and leverage the connection between vision and language, typically done via image captioning — images paired with human-authored textual descriptions of their content. One of the limitations of image captioning, however, is the lack of visual grounding, that is, localization on the image of the words in the textual description. To mitigate that, some previous works have a-posteriori drawn the bounding boxes for the nouns present in the description. In contrast, in localized narratives, every word in the textual description is grounded.
Different levels of grounding between image content and captioning. Left to Right: Caption to whole image (COCO); nouns to boxes (Flickr30k Entities); each word to a mouse trace segment (localized narratives). Image sources: COCO, Flickr30k Entities, and Sapa, Vietnam by Rama.
Localized narratives are generated by annotators who provide spoken descriptions of an image while they simultaneously move their mouse to hover over the regions they are describing. Voice annotation is at the core of our approach since it directly connects the description with the regions of the image it is referencing. To make the descriptions more accessible, the annotators manually transcribed their description, which was then aligned with the automatic speech transcription result. This recovers the timestamps for the description, ensuring that the three modalities (speech, text, and mouse trace) are correct and synchronized.
Alignment of manual and automatic transcriptions. Icons based on an original design from Freepik.
Speaking and pointing simultaneously are very intuitive, which allowed us to give the annotators very vague instructions about the task. This creates potential avenues of research for studying how people describe images. For example, we observed different styles when indicating the spatial extent of an object — circling, scratching, underlining, etc. — the study of which could bring valuable insights for the design of new user interfaces.
To get a sense of the amount of additional data these localized narratives represent, the total length of mouse traces is ~6400 km long, and if read aloud without stopping, all the narratives would take ~1.5 years to listen to!
New Visual Relationships, Human Actions, and Image-Level Annotations
In addition to the localized narratives, in Open Images V6 we increased the types of visual relationship annotations by an order of magnitude (up to 1.4k), adding for example “man riding a skateboard”, “man and woman holding hands”, and “dog catching a flying disk”.
People in images have been at the core of computer vision interests since its inception and understanding what those people are doing is of utmost importance for many applications. That is why Open Images V6 also includes 2.5M annotations of humans performing standalone actions, such as “jumping”, “smiling”, or “laying down”.
Finally, we also added 23.5M new human-verified image-level labels, reaching a total of 59.9M over nearly 20,000 categories.
Conclusion
Open Images V6 is a significant qualitative and quantitative step towards improving the unified annotations for image classification, object detection, visual relationship detection, and instance segmentation, and takes a novel approach in connecting vision and language with localized narratives. We hope that Open Images V6 will further stimulate progress towards genuine scene understanding.
