  Cross-modal retrieval of image-text and video-text is a prominent research
area in computer vision and natural language processing. However, there has
been insufficient attention given to cross-modal retrieval between human motion
and text, despite its wide-ranging applicability. To address this gap, we
utilize a concise yet effective dual-unimodal transformer encoder for tackling
this task. Recognizing that overlapping atomic actions in different human
motion sequences can lead to semantic conflicts between samples, we explore a
novel triplet loss function called DropTriple Loss. This loss function discards
false negative samples from the negative sample set and focuses on mining
remaining genuinely hard negative samples for triplet training, thereby
reducing violations they cause. We evaluate our model and approach on the
HumanML3D and KIT Motion-Language datasets. On the latest HumanML3D dataset, we
achieve a recall of 62.9% for motion retrieval and 71.5% for text retrieval
(both based on R@10). The source code for our approach is publicly available at
https://github.com/eanson023/rehamot.
