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[CVPR'23]Bi-Directional Feature Fusion Generative Adversarial Network for Ultra-High Resolution Pathological Image Virtual Re-Staining
The cost of pathological examination makes virtual re-staining of pathological images meaningful. However, due to the ultra-high resolution of pathological images, traditional virtual re-staining methods have to divide a WSI image into patches for model training and inference.
Kexin Sun
,
Zhineng Chen
,
Gongwei Wang
,
Jun Liu
,
Xiongjun Ye
,
Yu-Gang Jiang
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[CVPR'23]Prototypical Residual Networks for Anomaly Detection and Localization
Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance.
Hui Zhang
,
Zuxuan Wu
,
Zheng Wang
,
Zhineng Chen
,
Yu-Gang Jiang
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[IJCAI'22 oral]SVTR: Scene Text Recognition with a Single Visual Model
Dominant scene text recognition models commonly contain two building blocks, a visual model for feature extraction and a sequence model for text transcription. This hybrid architecture, although accurate, is complex and less efficient.
Yongkun Du
,
Zhineng Chen
,
Caiyan Jia
,
Xiaoting Yin
,
Tianlun Zheng
,
Chenxia Li
,
Yuning Du
,
Yu-Gang Jiang
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[ICASSP'22]Genre-Conditioned Long-Term 3D Dance Generation Driven by Music
Dancing to music is an artistic behavior of humans, however, letting machines generate dances from music is still challenging. Most existing works have been made progress in tackling the problem of motion prediction conditioned by music, yet they rarely consider the importance of the musical genre.
Yuhang Huang
,
Junjie Zhang
,
Shuyan Liu
,
Qian Bao
,
Dan Zeng
,
Zhineng Chen
,
YWu Liu
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[ICMR'21]Bag of Tricks for Building an Accurate and Slim Object Detector for Embedded Applications
Object detection is an essential computer vision task that possesses extensive application prospects in on-road applications. Copious novel methods have been proposed in this branch recently. However, the majority of them have high computational cost, making them intractable to be deployed on embedded devices.
Yongkun Du
,
Zhineng Chen
,
Caiyan Jia
,
Xuanya Li
,
Yu-Gang Jiang
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[MM'24]Decoder Pre-Training with only Text for Scene Text Recognition
Scene text recognition (STR) pre-training methods have achieved remarkable progress, primarily relying on synthetic datasets. However, the domain gap between synthetic and real images poses a challenge in acquiring feature representations that align well with images on real scenes, thereby limiting the performance of these methods.
Shuai Zhao
,
Yongkun Du
,
Zhineng Chen
,
Yu-Gang Jiang
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