  Procedure Planning in instructional videos entails generating a sequence of
action steps based on visual observations of the initial and target states.
Despite the rapid progress in this task, there remain several critical
challenges to be solved: (1) Adaptive procedures: Prior works hold an
unrealistic assumption that the number of action steps is known and fixed,
leading to non-generalizable models in real-world scenarios where the sequence
length varies. (2) Temporal relation: Understanding the step temporal relation
knowledge is essential in producing reasonable and executable plans. (3)
Annotation cost: Annotating instructional videos with step-level labels (i.e.,
timestamp) or sequence-level labels (i.e., action category) is demanding and
labor-intensive, limiting its generalizability to large-scale datasets.In this
work, we propose a new and practical setting, called adaptive procedure
planning in instructional videos, where the procedure length is not fixed or
pre-determined. To address these challenges we introduce Retrieval-Augmented
Planner (RAP) model. Specifically, for adaptive procedures, RAP adaptively
determines the conclusion of actions using an auto-regressive model
architecture. For temporal relation, RAP establishes an external memory module
to explicitly retrieve the most relevant state-action pairs from the training
videos and revises the generated procedures. To tackle high annotation cost,
RAP utilizes a weakly-supervised learning manner to expand the training dataset
to other task-relevant, unannotated videos by generating pseudo labels for
action steps. Experiments on CrossTask and COIN benchmarks show the superiority
of RAP over traditional fixed-length models, establishing it as a strong
baseline solution for adaptive procedure planning.
