  Multimodal alignment facilitates the retrieval of instances from one modality
when queried using another. In this paper, we consider a novel setting where
such an alignment is between (i) instruction steps that are depicted as
assembly diagrams (commonly seen in Ikea assembly manuals) and (ii) video
segments from in-the-wild videos; these videos comprising an enactment of the
assembly actions in the real world. To learn this alignment, we introduce a
novel supervised contrastive learning method that learns to align videos with
the subtle details in the assembly diagrams, guided by a set of novel losses.
To study this problem and demonstrate the effectiveness of our method, we
introduce a novel dataset: IAW for Ikea assembly in the wild consisting of 183
hours of videos from diverse furniture assembly collections and nearly 8,300
illustrations from their associated instruction manuals and annotated for their
ground truth alignments. We define two tasks on this dataset: First, nearest
neighbor retrieval between video segments and illustrations, and, second,
alignment of instruction steps and the segments for each video. Extensive
experiments on IAW demonstrate superior performances of our approach against
alternatives.
