4 research outputs found
Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition
The Deep Convolutional Neural Networks (CNNs) have obtained a great success
for pattern recognition, such as recognizing the texts in images. But existing
CNNs based frameworks still have several drawbacks: 1) the traditaional pooling
operation may lose important feature information and is unlearnable; 2) the
tradi-tional convolution operation optimizes slowly and the hierar-chical
features from different layers are not fully utilized. In this work, we address
these problems by developing a novel deep network model called
Fully-Convolutional Intensive Feature Flow Neural Network (IntensiveNet).
Specifically, we design a further dense block called intensive block to extract
the feature information, where the original inputs and two dense blocks are
connected tightly. To encode data appropriately, we present the concepts of
dense fusion block and further dense fusion opera-tions for our new intensive
block. By adding short connections to different layers, the feature flow and
coupling between layers are enhanced. We also replace the traditional
convolution by depthwise separable convolution to make the operation efficient.
To prevent important feature information being lost to a certain extent, we use
a convolution operation with stride 2 to replace the original pooling operation
in the customary transition layers. The recognition results on large-scale
Chinese string and MNIST datasets show that our IntensiveNet can deliver
enhanced recog-nition results, compared with other related deep models.Comment: Accepted by the 24th European Conference on Artificial Intelligence
(ECAI 2020). arXiv admin note: text overlap with arXiv:1912.0701
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
Single image deraining (SID) is an important and challenging topic in
emerging vision applications, and most of emerged deraining methods are
supervised relying on the ground truth (i.e., paired images) in recent years.
However, in practice it is rather common to have no un-paired images in real
deraining task, in such cases how to remove the rain streaks in an unsupervised
way will be a very challenging task due to lack of constraints between images
and hence suffering from low-quality recovery results. In this paper, we
explore the unsupervised SID task using unpaired data and propose a novel net
called Attention-guided Deraining by Constrained CycleGAN (or shortly,
DerainCycleGAN), which can fully utilize the constrained transfer learning
abilitiy and circulatory structure of CycleGAN. Specifically, we design an
unsu-pervised attention guided rain streak extractor (U-ARSE) that utilizes a
memory to extract the rain streak masks with two constrained cycle-consistency
branches jointly by paying attention to both the rainy and rain-free image
domains. As a by-product, we also contribute a new paired rain image dataset
called Rain200A, which is constructed by our network automatically. Compared
with existing synthesis datasets, the rainy streaks in Rain200A contains more
obvious and diverse shapes and directions. As a result, existing supervised
methods trained on Rain200A can perform much better for processing real rainy
images. Extensive experiments on synthesis and real datasets show that our net
is superior to existing unsupervised deraining networks, and is also very
competitive to other related supervised networks
Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional
Networks (DenseNet), have achieved great success for image representation by
discovering deep hierarchical information. However, most existing networks
simply stacks the convolutional layers and hence failing to fully discover
local and global feature information among layers. In this paper, we mainly
explore how to enhance the local and global dense feature flow by exploiting
hierarchical features fully from all the convolution layers. Technically, we
propose an efficient and effective CNN framework, i.e., Fast Dense Residual
Network (FDRN), for text recognition. To construct FDRN, we propose a new fast
residual dense block (f-RDB) to retain the ability of local feature fusion and
local residual learning of original RDB, which can reduce the computing efforts
at the same time. After fully learning local residual dense features, we
utilize the sum operation and several f-RDBs to define a new block termed
global dense block (GDB) by imitating the construction of dense blocks to learn
global dense residual features adaptively in a holistic way. Finally, we use
two convolution layers to construct a down-sampling block to reduce the global
feature size and extract deeper features. Extensive simulations show that FDRN
obtains the enhanced recognition results, compared with other related models.Comment: Please cite this work as: Zhao Zhang, Zemin Tang, Yang Wang, Zheng
Zhang, Choujun Zhan, Zhengjun Zha and Meng Wang, "Dense Residual Network:
Enhancing Global Dense Feature Flow for Character Recognition," Neural
Networks (NN), Feb 2021. arXiv admin note: text overlap with arXiv:1912.0701
Compressed DenseNet for Lightweight Character Recognition
Convolutional Recurrent Neural Network (CRNN) is a popular network for
recognizing texts in images. Advances like the variant of CRNN, such as Dense
Convolutional Network with Connectionist Temporal Classification, has reduced
the running time of the network, but exposing the inner computation cost and
weight size of the convolutional networks as a bottleneck. Specifically, the
DenseNet based models utilize the dense blocks as the core module, but the
inner features are combined in the form of concatenation in dense blocks. As
such, the number of channels of combined features delivered as the input of the
layers close to the output and the relevant computational cost grows rapidly
with the dense blocks getting deeper. This will severely bring heavy
computational cost and big weight size, which restrict the depth of dense
blocks. In this paper, we propose a compressed convolution block called
Lightweight Dense Block (LDB). To reduce the computing cost and weight size, we
re-define and re-design the way of combining internal features of the dense
blocks. LDB is a convolutional block similarly as dense block, but it can
reduce the computation cost and weight size to (1/L, 2/L), compared with
original ones, where L is the number of layers in blocks. Moreover, LDB can be
used to replace the original dense block in any DenseNet based models. Based on
the LDBs, we propose a Compressed DenseNet (CDenseNet) for the lightweight
character recognition. Extensive experiments demonstrate that CDenseNet can
effectively reduce the weight size while delivering the promising recognition
results