TextAug: Test time Text Augmentation for Multimodal Person
  Re-identification
Mulham Fawakherji Eduard Vazquez Pasquale Giampa Binod Bhattarai
Multimodal Person Reidentification is gaining popularity in the research
community due to its effectiveness compared to counter-part unimodal
frameworks. However, the bottleneck for multimodal deep learning is the need
for a large volume of multimodal training examples. Data augmentation
techniques such as cropping, flipping, rotation, etc. are often employed in the
image domain to improve the generalization of deep learning models. Augmenting
in other modalities than images, such as text, is challenging and requires
significant computational resources and external data sources. In this study,
we investigate the effectiveness of two computer vision data augmentation
techniques: cutout and cutmix, for text augmentation in multi-modal person
re-identification. Our approach merges these two augmentation strategies into
one strategy called CutMixOut which involves randomly removing words or
sub-phrases from a sentence (Cutout) and blending parts of two or more
sentences to create diverse examples (CutMix) with a certain probability
assigned to each operation. This augmentation was implemented at inference time
without any prior training. Our results demonstrate that the proposed technique
is simple and effective in improving the performance on multiple multimodal
person re-identification benchmarks.