nocaps: novel object captioning at scale
This addresses the challenge of enabling image captioning models to function in real-world scenarios by providing a benchmark to develop models that learn from alternative data sources like object detection datasets, though it is incremental as it builds on existing novel object captioning models.
The authors tackled the problem of image captioning models needing to learn a wider variety of visual concepts with less supervision by introducing 'nocaps', the first large-scale benchmark for novel object captioning, consisting of 166,100 captions for 15,100 images from OpenImages, with nearly 400 object classes having minimal training captions.
Image captioning models have achieved impressive results on datasets containing limited visual concepts and large amounts of paired image-caption training data. However, if these models are to ever function in the wild, a much larger variety of visual concepts must be learned, ideally from less supervision. To encourage the development of image captioning models that can learn visual concepts from alternative data sources, such as object detection datasets, we present the first large-scale benchmark for this task. Dubbed 'nocaps', for novel object captioning at scale, our benchmark consists of 166,100 human-generated captions describing 15,100 images from the OpenImages validation and test sets. The associated training data consists of COCO image-caption pairs, plus OpenImages image-level labels and object bounding boxes. Since OpenImages contains many more classes than COCO, nearly 400 object classes seen in test images have no or very few associated training captions (hence, nocaps). We extend existing novel object captioning models to establish strong baselines for this benchmark and provide analysis to guide future work on this task.