IIITD-20K: Dense captioning for Text-Image ReID
A V Subramanyam Niranjan Sundararajan Vibhu Dubey Brejesh Lall
Text-to-Image (T2I) ReID has attracted a lot of attention in the recent past.
CUHK-PEDES, RSTPReid and ICFG-PEDES are the three available benchmarks to
evaluate T2I ReID methods. RSTPReid and ICFG-PEDES comprise of identities from
MSMT17 but due to limited number of unique persons, the diversity is limited.
On the other hand, CUHK-PEDES comprises of 13,003 identities but has relatively
shorter text description on average. Further, these datasets are captured in a
restricted environment with limited number of cameras. In order to further
diversify the identities and provide dense captions, we propose a novel dataset
called IIITD-20K. IIITD-20K comprises of 20,000 unique identities captured in
the wild and provides a rich dataset for text-to-image ReID. With a minimum of
26 words for a description, each image is densely captioned. We further
synthetically generate images and fine-grained captions using Stable-diffusion
and BLIP models trained on our dataset. We perform elaborate experiments using
state-of-art text-to-image ReID models and vision-language pre-trained models
and present a comprehensive analysis of the dataset. Our experiments also
reveal that synthetically generated data leads to a substantial performance
improvement in both same dataset as well as cross dataset settings. Our dataset
is available at https://bit.ly/3pkA3Rj.