  Deep neural networks based object detectors have shown great success in a
variety of domains like autonomous vehicles, biomedical imaging, etc. It is
known that their success depends on a large amount of data from the domain of
interest. While deep models often perform well in terms of overall accuracy,
they often struggle in performance on rare yet critical data slices. For
example, data slices like "motorcycle at night" or "bicycle at night" are often
rare but very critical slices for self-driving applications and false negatives
on such rare slices could result in ill-fated failures and accidents. Active
learning (AL) is a well-known paradigm to incrementally and adaptively build
training datasets with a human in the loop. However, current AL based
acquisition functions are not well-equipped to tackle real-world datasets with
rare slices, since they are based on uncertainty scores or global descriptors
of the image. We propose TALISMAN, a novel framework for Targeted Active
Learning or object detectIon with rare slices using Submodular MutuAl
iNformation. Our method uses the submodular mutual information functions
instantiated using features of the region of interest (RoI) to efficiently
target and acquire data points with rare slices. We evaluate our framework on
the standard PASCAL VOC07+12 and BDD100K, a real-world self-driving dataset. We
observe that TALISMAN outperforms other methods by in terms of average
precision on rare slices, and in terms of mAP.
