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Pyramid Mask Text Detector
[article]
2019
arXiv
pre-print
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Scene text detection, an essential step of scene text recognition system, is to locate text instances in natural scene images automatically. Some recent attempts benefiting from Mask R-CNN formulate scene text detection task as an instance segmentation problem and achieve remarkable performance. In this paper, we present a new Mask R-CNN based framework named Pyramid Mask Text Detector (PMTD) to handle the scene text detection. Instead of binary text mask generated by the existing Mask R-CNN
arXiv:1903.11800v1
fatcat:zleabbqd3fcznhsok5dzx5tosi
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... ed methods, our PMTD performs pixel-level regression under the guidance of location-aware supervision, yielding a more informative soft text mask for each text instance. As for the generation of text boxes, PMTD reinterprets the obtained 2D soft mask into 3D space and introduces a novel plane clustering algorithm to derive the optimal text box on the basis of 3D shape. Experiments on standard datasets demonstrate that the proposed PMTD brings consistent and noticeable gain and clearly outperforms state-of-the-art methods. Specifically, it achieves an F-measure of 80.13% on ICDAR 2017 MLT dataset.
Counting dense objects in remote sensing images
[article]
2020
arXiv
pre-print
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Estimating accurate number of interested objects from a given image is a challenging yet important task. Significant efforts have been made to address this problem and achieve great progress, yet counting number of ground objects from remote sensing images is barely studied. In this paper, we are interested in counting dense objects from remote sensing images. Compared with object counting in natural scene, this task is challenging in following factors: large scale variation, complex cluttered
arXiv:2002.05928v1
fatcat:vezcj6ersfgw3bft3wipdqsbiy
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... ackground and orientation arbitrariness. More importantly, the scarcity of data severely limits the development of research in this field. To address these issues, we first construct a large-scale object counting dataset based on remote sensing images, which contains four kinds of objects: buildings, crowded ships in harbor, large-vehicles and small-vehicles in parking lot. We then benchmark the dataset by designing a novel neural network which can generate density map of an input image. The proposed network consists of three parts namely convolution block attention module (CBAM), scale pyramid module (SPM) and deformable convolution module (DCM). Experiments on the proposed dataset and comparisons with state of the art methods demonstrate the challenging of the proposed dataset, and superiority and effectiveness of our method.
SparseTT: Visual Tracking with Sparse Transformers
[article]
2022
arXiv
pre-print
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Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted by background. In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the
arXiv:2205.03776v1
fatcat:djchdede4vbrxgq5gdp32yisza
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... ch regions, which enables a much accurate tracking. Furthermore, we introduce a double-head predictor to boost the accuracy of foreground-background classification and regression of target bounding boxes, which further improve the tracking performance. Extensive experiments show that, without bells and whistles, our method significantly outperforms the state-of-the-art approaches on LaSOT, GOT-10k, TrackingNet, and UAV123, while running at 40 FPS. Notably, the training time of our method is reduced by 75% compared to that of TransT. The source code and models are available at https://github.com/fzh0917/SparseTT.
EDTER: Edge Detection with Transformer
[article]
2022
arXiv
pre-print
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Convolutional neural networks have made significant progresses in edge detection by progressively exploring the context and semantic features. However, local details are gradually suppressed with the enlarging of receptive fields. Recently, vision transformer has shown excellent capability in capturing long-range dependencies. Inspired by this, we propose a novel transformer-based edge detector, Edge Detection TransformER (EDTER), to extract clear and crisp object boundaries and meaningful
arXiv:2203.08566v1
fatcat:vb2gjughizglxnnx3itvpi6muq
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... by exploiting the full image context information and detailed local cues simultaneously. EDTER works in two stages. In Stage I, a global transformer encoder is used to capture long-range global context on coarse-grained image patches. Then in Stage II, a local transformer encoder works on fine-grained patches to excavate the short-range local cues. Each transformer encoder is followed by an elaborately designed Bi-directional Multi-Level Aggregation decoder to achieve high-resolution features. Finally, the global context and local cues are combined by a Feature Fusion Module and fed into a decision head for edge prediction. Extensive experiments on BSDS500, NYUDv2, and Multicue demonstrate the superiority of EDTER in comparison with state-of-the-arts.
Dynamical switching of lasing emission by exceptional point modulation in coupled microcavities
[article]
2019
arXiv
pre-print
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In a non-Hermitian optical system with loss and gain, an exceptional point (EP) will arise under specific parameters where the eigenvalues and eigenstates exhibit simultaneous coalescence. Here we report a dynamical switching of lasing behavior in a non-Hermitian system composed of coupled microcavities by modulating the EPs. Utilizing the effect of gain, loss and coupling on the eigenstates of coupled microcavities, the evolution path of the eigenvalues related to the laser emission
arXiv:1912.11765v1
fatcat:vbyprhkfofgerfw5vafhbf2iri
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... tics can be modulated. As a result, the lasing emission property of the coupled cavities exhibits an dynamical switching behavior, which can also be effectively controlled by tuning the gain and loss of the cavities. Moreover, the evolution behavior in a more complicated system composed of three coupled microcavities is investigated, which shows a better tunability compared with the two-microcavity system. Our results have correlated the EPs in non-Hermitian system with lasing emission in complex microcavity systems, which shows great potential for realizing dynamical, ultrafast and multifunctional optoelectronic devices for on-chip integrations.
Unsupervised Change Detection for Multispectral Remote Sensing Images Using Random Walks
2017
Remote Sensing
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Lining Liu and Qingjie Liu performed the experiments; Qingjie Liu analyzed the data; Yunhong Wang contributed analysis tools; Qingjie Liu and Lining Liu wrote the paper.Conflicts of Interest:The authors ...
Author Contributions: Qingjie Liu and Lining Liu conceived and designed the experiments. ...
doi:10.3390/rs9050438
fatcat:g66xktqcrragbc4vbxstvxny7e
Feature Map Pooling for Cross-View Gait Recognition Based on Silhouette Sequence Images
[article]
2017
arXiv
pre-print
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In this paper, we develop a novel convolutional neural network based approach to extract and aggregate useful information from gait silhouette sequence images instead of simply representing the gait process by averaging silhouette images. The network takes a pair of arbitrary length sequence images as inputs and extracts features for each silhouette independently. Then a feature map pooling strategy is adopted to aggregate sequence features. Subsequently, a network which is similar to Siamese
arXiv:1711.09358v1
fatcat:6jljlh6w6zes7fklkpmrwjpewm
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... twork is designed to perform recognition. The proposed network is simple and easy to implement and can be trained in an end-to-end manner. Cross-view gait recognition experiments are conducted on OU-ISIR large population dataset. The results demonstrate that our network can extract and aggregate features from silhouette sequence effectively. It also achieves significant equal error rates and comparable identification rates when compared with the state of the art.
Ultrasound Video Summarization using Deep Reinforcement Learning
[article]
2020
arXiv
pre-print
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Video is an essential imaging modality for diagnostics, e.g. in ultrasound imaging, for endoscopy, or movement assessment. However, video hasn't received a lot of attention in the medical image analysis community. In the clinical practice, it is challenging to utilise raw diagnostic video data efficiently as video data takes a long time to process, annotate or audit. In this paper we introduce a novel, fully automatic video summarization method that is tailored to the needs of medical video
arXiv:2005.09531v1
fatcat:7smho4n6lber3ccxzwvvfac6yi
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... . Our approach is framed as reinforcement learning problem and produces agents focusing on the preservation of important diagnostic information. We evaluate our method on videos from fetal ultrasound screening, where commonly only a small amount of the recorded data is used diagnostically. We show that our method is superior to alternative video summarization methods and that it preserves essential information required by clinical diagnostic standards.
Co-Saliency Detection with Co-Attention Fully Convolutional Network
[article]
2020
arXiv
pre-print
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(Corresponding author: Qingjie Liu) Guangshuai Gao, Qingjie Liu and Yunhong Wang are with the State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Xueyuan Road, Haidian District ...
Liu et al. ...
arXiv:2008.08909v1
fatcat:ykgayihxrvcp5hac3gv545h7li
Tunnel surrounding rock stability prediction using improved KNN algorithm
2020
Journal of Vibroengineering
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Liu et al. ...
doi:10.21595/jve.2020.21427
fatcat:oh4ljcxu5jem3dla3aqgtafx54
Isolation and identification of a halophilic and alkaliphilic microalgal strain
2019
PeerJ
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., 2011; Wei, Takano & Liu, 2012) . ...
Full-size DOI: 10.7717/peerj.7189/fig-4
Liu et al. (2019), PeerJ, DOI 10.7717/peerj.7189 8/10
Liu et al. (2019), PeerJ, DOI 10.7717/peerj.7189 10/10 ...
Qingjie Guan conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, authored or reviewed drafts of the paper, approved ...
doi:10.7717/peerj.7189
pmid:31275763
pmcid:PMC6596407
fatcat:ykxrhodmuzbo7cbnuyaqkxzaxm
Study on Flow in Fractured Porous Media Using Pore-Fracture Network Modeling
2017
Energies
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Liu et al. [36] developed a fluid-solid coupling model for low-permeability fractured reservoirs. ...
Author Contributions: Xuhui Zhang, Xiaobing Lu and Qingjie Liu conceived of the presented idea. Haijiao Liu performed the computations and verified the methods. ...
Haijiao Liu and Xuhui Zhang wrote and revised the article. All authors discussed the results and contributed to the final manuscript. ...
doi:10.3390/en10121984
fatcat:jmlnjpevxfayfdtwbs7fod6yhm
Visual and Textual Sentiment Analysis Using Deep Fusion Convolutional Neural Networks
[article]
2017
arXiv
pre-print
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Sentiment analysis is attracting more and more attentions and has become a very hot research topic due to its potential applications in personalized recommendation, opinion mining, etc. Most of the existing methods are based on either textual or visual data and can not achieve satisfactory results, as it is very hard to extract sufficient information from only one single modality data. Inspired by the observation that there exists strong semantic correlation between visual and textual data in
arXiv:1711.07798v1
fatcat:wwg7euw24fcldbzozdseokerc4
more »
... cial medias, we propose an end-to-end deep fusion convolutional neural network to jointly learn textual and visual sentiment representations from training examples. The two modality information are fused together in a pooling layer and fed into fully-connected layers to predict the sentiment polarity. We evaluate the proposed approach on two widely used data sets. Results show that our method achieves promising result compared with the state-of-the-art methods which clearly demonstrate its competency.
Qingjie Fuzheng granules inhibit colorectal cancer cell growth by the PI3K/AKT and ERK pathways
2019
World Journal of Gastrointestinal Oncology
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Qingjie Fuzheng granules (QFGs) are part of a traditional Chinese medicine formula, which has been widely used and found to be clinically effective with few side effects in various cancer treatments, including ...
QFGs: Qingjie Fuzheng granules; FACS: Fluorescence activated cell sorting. ...
QFGs: Qingjie Fuzheng granules; PI: Propidium iodide; FITC: Fluorescein isothiocyanate. ...
doi:10.4251/wjgo.v11.i5.377
pmid:31139308
pmcid:PMC6522764
fatcat:26eplsija5efrdd3jmlb4ubd7m
CNN-based Density Estimation and Crowd Counting: A Survey
[article]
2020
arXiv
pre-print
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Furthermore, Liu et al. ...
), Northwestern Polytechnical University, Xi'an 710072, Shanxi, China (email: gjy3035@gmail.com;crabwq@gmail.com) * Corresponding author: Qingjie Liu Hangzhou,
310051,China
(email:
gaoguangshuai1990 ...
arXiv:2003.12783v1
fatcat:uqsoismxkzft7audwvdpr3dt7q
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