  This work investigates the problem of sketch-guided object localization
(SGOL), where human sketches are used as queries to conduct the object
localization in natural images. In this cross-modal setting, we first
contribute with a tough-to-beat baseline that without any specific SGOL
training is able to outperform the previous works on a fixed set of classes.
The baseline is useful to analyze the performance of SGOL approaches based on
available simple yet powerful methods. We advance prior arts by proposing a
sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a
hard classification and alleviates the domain gap between sketches and images
to localize object instances. Although the main goal of SGOL is focused on
object detection, we explored its natural extension to sketch-guided instance
segmentation. This novel task allows to move towards identifying the objects at
pixel level, which is of key importance in several applications. We
experimentally demonstrate that our model and its variants significantly
advance over previous state-of-the-art results. All training and testing code
of our model will be released to facilitate future
research{{https://github.com/priba/sgol_wild}}.
