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Found 7 papers, 2 papers with code
Masked and Permuted Implicit Context Learning for Scene Text Recognition
We utilize the training procedure of PLM, and to integrate MLM, we incorporate word length information into the decoding process and replace the undetermined characters with mask tokens.
Multi-View Correlation Distillation for Incremental Object Detection
Due to the storage burden and the privacy of old data, sometimes it is impractical to train the model from scratch with both old and new data.
Two-Level Residual Distillation based Triple Network for Incremental Object Detection
Modern object detection methods based on convolutional neural network suffer from severe catastrophic forgetting in learning new classes without original data.
Self-Training for Domain Adaptive Scene Text Detection
To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images.
SEED: Semantics Enhanced Encoder-Decoder Framework for Scene Text Recognition
Scene text recognition is a hot research topic in computer vision.
Video Cloze Procedure for Self-Supervised Spatio-Temporal Learning
As a proxy task, it converts rich self-supervised representations into video clip operations (options), which enhances the flexibility and reduces the complexity of representation learning.
Ranked #11 on
Self-supervised Video Retrieval
on HMDB51
Curved Text Detection in Natural Scene Images with Semi- and Weakly-Supervised Learning
The performance of the proposed method is comparable with the state-of-the-art methods with only 10% pixel-level annotated data and 90% rectangle-level weakly annotated data.
