Measuring Progress in Fine-grained Vision-and-Language Understanding
Emanuele Bugliarello Laurent Sartran Aishwarya Agrawal Lisa Anne Hendricks Aida Nematzadeh
While pretraining on large-scale image-text data from the Web has facilitated
rapid progress on many vision-and-language (V&L) tasks, recent work has
demonstrated that pretrained models lack "fine-grained" understanding, such as
the ability to recognise relationships, verbs, and numbers in images. This has
resulted in an increased interest in the community to either develop new
benchmarks or models for such capabilities. To better understand and quantify
progress in this direction, we investigate four competitive V&L models on four
fine-grained benchmarks. Through our analysis, we find that X-VLM (Zeng et al.,
2022) consistently outperforms other baselines, and that modelling innovations
can impact performance more than scaling Web data, which even degrades
performance sometimes. Through a deeper investigation of X-VLM, we highlight
the importance of both novel losses and rich data sources for learning
fine-grained skills. Finally, we inspect training dynamics, and discover that
for some tasks, performance peaks early in training or significantly
fluctuates, never converging.