  Existing methods for arbitrary-shaped text detection in natural scenes face
two critical issues, i.e., 1) fracture detections at the gaps in a text
instance; and 2) inaccurate detections of arbitrary-shaped text instances with
diverse background context. To address these issues, we propose a novel method
named Intra- and Inter-Instance Collaborative Learning (I3CL). Specifically, to
address the first issue, we design an effective convolutional module with
multiple receptive fields, which is able to collaboratively learn better
character and gap feature representations at local and long ranges inside a
text instance. To address the second issue, we devise an instance-based
transformer module to exploit the dependencies between different text instances
and a global context module to exploit the semantic context from the shared
background, which are able to collaboratively learn more discriminative text
feature representation. In this way, I3CL can effectively exploit the intra-
and inter-instance dependencies together in a unified end-to-end trainable
framework. Besides, to make full use of the unlabeled data, we design an
effective semi-supervised learning method to leverage the pseudo labels via an
ensemble strategy. Without bells and whistles, experimental results show that
the proposed I3CL sets new state-of-the-art results on three challenging public
benchmarks, i.e., an F-measure of 77.5% on ICDAR2019-ArT, 86.9% on Total-Text,
and 86.4% on CTW-1500. Notably, our I3CL with the ResNeSt-101 backbone ranked
1st place on the ICDAR2019-ArT leaderboard. The source code will be available
at https://github.com/ViTAE-Transformer/ViTAE-Transformer-Scene-Text-Detection.
