Haystack: A Panoptic Scene Graph Dataset to Evaluate Rare Predicate Classes
This addresses the issue of unreliable metrics for rare classes in scene graph generation, primarily for researchers in computer vision, though it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of long-tail distributions in scene graph datasets by constructing Haystack, a panoptic scene graph dataset with explicit negative annotations, resulting in a benchmark that enables reliable evaluation of rare predicate classes.
Current scene graph datasets suffer from strong long-tail distributions of their predicate classes. Due to a very low number of some predicate classes in the test sets, no reliable metrics can be retrieved for the rarest classes. We construct a new panoptic scene graph dataset and a set of metrics that are designed as a benchmark for the predictive performance especially on rare predicate classes. To construct the new dataset, we propose a model-assisted annotation pipeline that efficiently finds rare predicate classes that are hidden in a large set of images like needles in a haystack. Contrary to prior scene graph datasets, Haystack contains explicit negative annotations, i.e. annotations that a given relation does not have a certain predicate class. Negative annotations are helpful especially in the field of scene graph generation and open up a whole new set of possibilities to improve current scene graph generation models. Haystack is 100% compatible with existing panoptic scene graph datasets and can easily be integrated with existing evaluation pipelines. Our dataset and code can be found here: https://lorjul.github.io/haystack/. It includes annotation files and simple to use scripts and utilities, to help with integrating our dataset in existing work.