No abstract

In this paper, we investigate the interesting yet challenging problem of camouflaged instance segmentation. To this end, we first annotate the available CAMO dataset at the instance level. We also embed the data augmentation in order to increase the number of training samples. Then, we train different state-of-the-art instance segmentation on the CAMO-instance data. Last but not least, we develop an interactive user interface which demonstrates the performance of different state-of-the-art instance segmentation methods on the task of camouflaged instance segmentation. The users are able to compare the results of different methods on the given input images. Our work is expected to push the envelope of the camouflage analysis problem.

In this paper, we study the global regularity estimates in Lorentz spaces for gradients of solutions to quasilinear elliptic equations with measure data of the form [Formula: see text] where [Formula: see text] is a finite signed Radon measure in [Formula: see text], [Formula: see text] is a bounded domain such that its complement [Formula: see text] is uniformly [Formula: see text]-thick and [Formula: see text] is a Carathéodory vector-valued function satisfying growth and monotonicity conditions for the strongly singular case [Formula: see text]. Our result extends the earlier results [19,22] to the strongly singular case [Formula: see text] and a recent result [18] by considering rough conditions on the domain [Formula: see text] and the nonlinearity [Formula: see text].