  In the development of science, accurate and reproducible documentation of the
experimental process is crucial. Automatic recognition of the actions in
experiments from videos would help experimenters by complementing the recording
of experiments. Towards this goal, we propose FineBio, a new fine-grained video
dataset of people performing biological experiments. The dataset consists of
multi-view videos of 32 participants performing mock biological experiments
with a total duration of 14.5 hours. One experiment forms a hierarchical
structure, where a protocol consists of several steps, each further decomposed
into a set of atomic operations. The uniqueness of biological experiments is
that while they require strict adherence to steps described in each protocol,
there is freedom in the order of atomic operations. We provide hierarchical
annotation on protocols, steps, atomic operations, object locations, and their
manipulation states, providing new challenges for structured activity
understanding and hand-object interaction recognition. To find out challenges
on activity understanding in biological experiments, we introduce baseline
models and results on four different tasks, including (i) step segmentation,
(ii) atomic operation detection (iii) object detection, and (iv)
manipulated/affected object detection. Dataset and code are available from
https://github.com/aistairc/FineBio.
