Papers › I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection
I3CL:Intra- and Inter-Instance Collaborative Learning for Arbitrary-shaped Scene Text Detection
Bo Du, Jian Ye, Jing Zhang, Juhua Liu, DaCheng Tao
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Scene Text Detection | SCUT-CTW1500 | I3CL + SSL | F-Measure | 86.5 | #5 of 17 | Archive leaderboard | report |
| Scene Text Detection | SCUT-CTW1500 | I3CL + SSL | Precision | 88.4 | #5 of 17 | Archive leaderboard | report |
| Scene Text Detection | SCUT-CTW1500 | I3CL + SSL | Recall | 84.6 | #5 of 17 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | I3CL + SSL(ResNet-50) | F-Measure | 86.9% | #6 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | I3CL + SSL(ResNet-50) | Precision | 89.8 | #6 of 27 | Archive leaderboard | report |
| Scene Text Detection | Total-Text | I3CL + SSL(ResNet-50) | Recall | 84.2 | #6 of 27 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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