ConQA is a dataset created using the intersection between VisualGenome and MS-COCO. The goal of this dataset is to provide a new benchmark for text to image retrieval using short and less descriptive queries than the commonly use captions from MS-COCO or Flicker. ConQA consists of 80 queries divided into 50 conceptual and 30 descriptive queries. A descriptive query mentions some of the objects in the image, for instance, people chopping vegetables. While, a conceptual query does not mention objects or only refers to objects in a general context, e.g., working class life.

Dataset generation

For the dataset generation, we followed a 3 step workflow: filtering images, generating queries and seeding relevant, and crowd-sourcing extended annotations.

Filtering images

The first step is focused on filtering images that have meaningful scene graphs and captions. To filter the images, we used the following procedure:

• The image should have one or more captions. Hence, we discarded the YFCC images with no caption, obtaining images from the MS-COCO subset of Visual Genome.
• The image should describe a complex scene with multiple objects. We filtered all the scene graphs that did not contain any edges. images pass this filter.
• The relationships should be verbs and not contain nouns or pronouns. To detect this, we generated sentences for each edge as a concatenation of the words on the labels of the nodes and the relationship and applied Part of Speech tagging. We performed the POS Tagging using the model en_core_web_sm provided by SpaCy. We filter all scene graphs that contain an edge not tagged as a verb or that the tag is not in an ad-hoc list of allowed non-verb keywords. The allowed keywords are top, front, end, side, edge, middle, rear, part, bottom, under, next, left, right, center, background, back, and parallel. We allowed these keywords as they represent positional relationships between objects. After filtering, we obtain images.

##Generating Queries To generate ConQA, the dataset authors worked in three pairs and acted as annotators to manually design the queries, namely 50 conceptual and 30 descriptive queries. After that, we proceeded to use the model "ViT-B/32" from CLIP to find relevant images. For conceptual queries, it was challenging to find relevant images directly, so alternative proxy queries were used to identify an initial set of relevant images. These images are the seed for finding other relevant images that were annotated through Amazon Mechanical Turk.

Annotation crowdsourcing

Having the initial relevant set defined by the dataset authors, we expanded the relevant candidates by looking into the top-100 visually closest images according to a pre-trained ResNet152 model for each query. As a result, we increase the number of potentially relevant images to analyze without adding human bias to the task.

After selecting the images to annotate, we set up a set of Human Intelligence Tasks (HITs) on Amazon Mechanical Turk. Each task consisted of a query and 5 potentially relevant images. Then, the workers were instructed to determine whether each image is relevant for the given query. If they were not sure, they could alternatively mark the image as “Unsure”. To reduce presentation bias, we randomize the order of images and the options. Additionally, we include validation tasks with control images to ensure a minimum quality in the annotation process, so workers failing 70% or more of validation queries were excluded.