ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab
This work addresses the problem of replicating research results for molecular biologists, but it is incremental as it focuses on dataset creation and benchmarking without introducing new methods.
The authors tackled the reproducibility crisis in molecular biology by creating ProBio, a multimodal dataset with fine-grained hierarchical annotations for activity understanding in BioLab, and they evaluated existing video understanding models to highlight their limitations in this domain.
The challenge of replicating research results has posed a significant impediment to the field of molecular biology. The advent of modern intelligent systems has led to notable progress in various domains. Consequently, we embarked on an investigation of intelligent monitoring systems as a means of tackling the issue of the reproducibility crisis. Specifically, we first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective. This dataset comprises fine-grained hierarchical annotations intended for the purpose of studying activity understanding in BioLab. Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings. Finally, we provide a thorough experimental evaluation of contemporary video understanding models and highlight their limitations in this specialized domain to identify potential avenues for future research. We hope ProBio with associated benchmarks may garner increased focus on modern AI techniques in the realm of molecular biology.