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Article
Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation
Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper explores applying the consist...
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Article
Serum PCSK9 is positively correlated with disease activity and Th17 cells, while its short-term decline during treatment reflects desirable outcomes in ankylosing spondylitis patients
Proprotein convertase subtilisin/kexin type 9 (PCSK9) participates in the autoimmune disease pathology by regulating T helper (Th) cell differentiation, NF-κB pathway, toll-like receptor 4, etc. This study int...
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Chapter and Conference Paper
CoactSeg: Learning from Heterogeneous Data for New Multiple Sclerosis Lesion Segmentation
New lesion segmentation is essential to estimate the disease progression and therapeutic effects during multiple sclerosis (MS) clinical treatments. However, the expensive data acquisition and expert annotatio...
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Chapter and Conference Paper
Cross-Adversarial Local Distribution Regularization for Semi-supervised Medical Image Segmentation
Medical semi-supervised segmentation is a technique where a model is trained to segment objects of interest in medical images with limited annotated data. Existing semi-supervised segmentation methods are usua...
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Article
Learning to Collocate Visual-Linguistic Neural Modules for Image Captioning
Humans tend to decompose a sentence into different parts like sth do sth at someplace and then fill each part with certain content. Inspired by this, we follow the principle of modular design to propose a novel i...
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Chapter and Conference Paper
Exploring Smoothness and Class-Separation for Semi-supervised Medical Image Segmentation
Semi-supervised segmentation remains challenging in medical imaging since the amount of annotated medical data is often scarce and there are many blurred pixels near the adhesive edges or in the low-contrast r...
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Chapter and Conference Paper
Object-Compositional Neural Implicit Surfaces
The neural implicit representation has shown its effectiveness in novel view synthesis and high-quality 3D reconstruction from multi-view images. However, most approaches focus on holistic scene representation...
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Chapter and Conference Paper
Sem2NeRF: Converting Single-View Semantic Masks to Neural Radiance Fields
Image translation and manipulation have gain increasing attention along with the rapid development of deep generative models. Although existing approaches have brought impressive results, they mainly operated ...
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Chapter and Conference Paper
Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
Weakly supervised point cloud segmentation, i.e. semantically segmenting a point cloud with only a few labeled points in the whole 3D scene, is highly desirable due to the heavy burden of collecting abundant d...
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Chapter and Conference Paper
Multimodal Transformer with Variable-Length Memory for Vision-and-Language Navigation
Vision-and-Language Navigation (VLN) is a task that an agent is required to follow a language instruction to navigate to the goal position, which relies on the ongoing interactions with the environment during ...
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Chapter and Conference Paper
ExtrudeNet: Unsupervised Inverse Sketch-and-Extrude for Shape Parsing
Sketch-and-extrude is a common and intuitive modeling process in computer aided design. This paper studies the problem of learning the shape given in the form of point clouds by “inverse” sketch-and-extrude. W...
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Article
Visiting the Invisible: Layer-by-Layer Completed Scene Decomposition
Existing scene understanding systems mainly focus on recognizing the visible parts of a scene, ignoring the intact appearance of physical objects in the real-world. Concurrently, image completion has aimed to ...
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Article
Pluralistic Free-Form Image Completion
Image completion involves filling plausible contents to missing regions in images. Current image completion methods produce only one result for a given masked image, although there may be many reasonable possi...
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Article
JÂA-Net: Joint Facial Action Unit Detection and Face Alignment Via Adaptive Attention
Facial action unit (AU) detection and face alignment are two highly correlated tasks, since facial landmarks can provide precise AU locations to facilitate the extraction of meaningful local features for AU de...
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Chapter and Conference Paper
Semi-supervised Left Atrium Segmentation with Mutual Consistency Training
Semi-supervised learning has attracted great attention in the field of machine learning, especially for medical image segmentation tasks, since it alleviates the heavy burden of collecting abundant densely ann...
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Chapter and Conference Paper
Finding It at Another Side: A Viewpoint-Adapted Matching Encoder for Change Captioning
Change Captioning is a task that aims to describe the difference between images with natural language. Most existing methods treat this problem as a difference judgment without the existence of distractors, su...
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Chapter and Conference Paper
Splitting Vs. Merging: Mining Object Regions with Discrepancy and Intersection Loss for Weakly Supervised Semantic Segmentation
In this paper we focus on the task of weakly-supervised semantic segmentation supervised with image-level labels. Since the pixel-level annotation is not available in the training process, we rely on region mi...
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Chapter and Conference Paper
Learning Progressive Joint Propagation for Human Motion Prediction
Despite the great progress in human motion prediction, it remains a challenging task due to the complicated structural dynamics of human behaviors. In this paper, we address this problem in three aspects. Firs...
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Chapter and Conference Paper
Improving Deep Lesion Detection Using 3D Contextual and Spatial Attention
Lesion detection from computed tomography (CT) scans is challenging compared to natural object detection because of two major reasons: small lesion size and small inter-class variation. Firstly, the lesions us...
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Article
An adaptive RBF-HDMR modeling approach under limited computational budget
The metamodel-based high-dimensional model representation (e.g., RBF-HDMR) has recently been proven to be very promising for modeling high dimensional functions. A frequently encountered scenario in practical ...
