74 research outputs found
Aggregated Text Transformer for Scene Text Detection
This paper explores the multi-scale aggregation strategy for scene text
detection in natural images. We present the Aggregated Text TRansformer(ATTR),
which is designed to represent texts in scene images with a multi-scale
self-attention mechanism. Starting from the image pyramid with multiple
resolutions, the features are first extracted at different scales with shared
weight and then fed into an encoder-decoder architecture of Transformer. The
multi-scale image representations are robust and contain rich information on
text contents of various sizes. The text Transformer aggregates these features
to learn the interaction across different scales and improve text
representation. The proposed method detects scene texts by representing each
text instance as an individual binary mask, which is tolerant of curve texts
and regions with dense instances. Extensive experiments on public scene text
detection datasets demonstrate the effectiveness of the proposed framework
DDT: Dual-branch Deformable Transformer for Image Denoising
Transformer is beneficial for image denoising tasks since it can model
long-range dependencies to overcome the limitations presented by inductive
convolutional biases. However, directly applying the transformer structure to
remove noise is challenging because its complexity grows quadratically with the
spatial resolution. In this paper, we propose an efficient Dual-branch
Deformable Transformer (DDT) denoising network which captures both local and
global interactions in parallel. We divide features with a fixed patch size and
a fixed number of patches in local and global branches, respectively. In
addition, we apply deformable attention operation in both branches, which helps
the network focus on more important regions and further reduces computational
complexity. We conduct extensive experiments on real-world and synthetic
denoising tasks, and the proposed DDT achieves state-of-the-art performance
with significantly fewer computational costs.Comment: The code is avaliable at: https://github.com/Merenguelkl/DD
Progressive Scene Text Erasing with Self-Supervision
Scene text erasing seeks to erase text contents from scene images and current
state-of-the-art text erasing models are trained on large-scale synthetic data.
Although data synthetic engines can provide vast amounts of annotated training
samples, there are differences between synthetic and real-world data. In this
paper, we employ self-supervision for feature representation on unlabeled
real-world scene text images. A novel pretext task is designed to keep
consistent among text stroke masks of image variants. We design the Progressive
Erasing Network in order to remove residual texts. The scene text is erased
progressively by leveraging the intermediate generated results which provide
the foundation for subsequent higher quality results. Experiments show that our
method significantly improves the generalization of the text erasing task and
achieves state-of-the-art performance on public benchmarks
- …