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Chapter and Conference Paper
YONA: You Only Need One Adjacent Reference-Frame for Accurate and Fast Video Polyp Detection
Accurate polyp detection is essential for assisting clinical rectal cancer diagnoses. Colonoscopy videos contain richer information than still images, making them a valuable resource for deep learning methods....
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Chapter and Conference Paper
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation
To further reduce the cost of semi-supervised domain adaptation (SSDA) labeling, a more effective way is to use active learning (AL) to annotate a selected subset with specific properties. However, domain adap...
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Chapter and Conference Paper
Toward Clinically Assisted Colorectal Polyp Recognition via Structured Cross-Modal Representation Consistency
The colorectal polyps classification is a critical clinical examination. To improve the classification accuracy, most computer-aided diagnosis algorithms recognize colorectal polyps by adopting Narrow-Band Ima...
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Chapter and Conference Paper
Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration
Weakly Supervised Object Localization (WSOL), which aims to localize objects by only using image-level labels, has attracted much attention because of its low annotation cost in real applications. Recent studi...
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Chapter and Conference Paper
2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds
As camera and LiDAR sensors capture complementary information in autonomous driving, great efforts have been made to conduct semantic segmentation through multi-modality data fusion. However, fusion-based appr...
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Chapter and Conference Paper
Multi-compound Transformer for Accurate Biomedical Image Segmentation
The recent vision transformer (i.e. for image classification) learns non-local attentive interaction of different patch tokens. However, prior arts miss learning the cross-scale dependencies of different pixels, ...
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Chapter and Conference Paper
Shallow Attention Network for Polyp Segmentation
Accurate polyp segmentation is of great importance for colorectal cancer diagnosis. However, even with a powerful deep neural network, there still exists three big challenges that impede the development of pol...
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Article
SSN: Learning Sparse Switchable Normalization via SparsestMax
Normalization method deals with parameters training of convolution neural networks (CNNs) in which there are often multiple convolution layers. Despite the fact that layers in CNN are not homogeneous in the ro...
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Chapter and Conference Paper
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to-RGB map** problem, wher...
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Chapter and Conference Paper
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
Aggregating multi-level feature representation plays a critical role in achieving robust volumetric medical image segmentation, which is important for the auxiliary diagnosis and treatment. Unlike the recent n...
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Chapter and Conference Paper
Towards Content-Independent Multi-Reference Super-Resolution: Adaptive Pattern Matching and Feature Aggregation
Recovering realistic textures from a largely down-sampled low resolution (LR) image with complicated patterns is a challenging problem in image super-resolution. This work investigates a novel multi-reference ...
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Article
Learning deep representations for semantic image parsing: a comprehensive overview
Semantic image parsing, which refers to the process of decomposing images into semantic regions and constructing the structure representation of the input, has recently aroused widespread interest in the field...
