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Found 18 papers, 4 papers with code
Contrastive Disentangled Learning on Graph for Node Classification
Inspired by the success of contrastive learning, we propose a novel framework for contrastive disentangled learning on graphs, employing a disentangled graph encoder and two carefully crafted self-supervision signals.
Variational Disentangled Graph Auto-Encoders for Link Prediction
The proposed framework infers the latent factors that cause edges in the graph and disentangles the representation into multiple channels corresponding to unique latent factors, which contributes to improving the performance of link prediction.
Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment
In this paper, we reveal that the scale factor has a statistically significant impact on subjective quality scores of SR images, indicating that the scale information can be used to guide the task of blind SR IQA.
RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment
Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA.
Parotid Gland MRI Segmentation Based on Swin-Unet and Multimodal Images
Preoperative tumor localization, differential diagnosis, and subsequent selection of appropriate treatment for parotid gland tumors are critical.
Mutual Attention-based Hybrid Dimensional Network for Multimodal Imaging Computer-aided Diagnosis
Recent works on Multimodal 3D Computer-aided diagnosis have demonstrated that obtaining a competitive automatic diagnosis model when a 3D convolution neural network (CNN) brings more parameters and medical images are scarce remains nontrivial and challenging.
ImageSubject: A Large-scale Dataset for Subject Detection
We present a new dataset with the goal of training models to understand the layout of the objects and the context of the image then to find the main subjects among them.
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective
Under this brand-new scenario, we propose Distortion Relation guided Transfer Learning (DRTL) for the few-shot RealSR by transferring the rich restoration knowledge from auxiliary distortions (i. e., synthetic distortions) to the target RealSR under the guidance of distortion relation.
Fine-Grained Control of Artistic Styles in Image Generation
The style vectors are fed to the generator and discriminator to achieve fine-grained control.
Adaptive Hypergraph Convolutional Network for No-Reference 360-degree Image Quality Assessment
In the hypergraph construction, we build a location-based hyperedge and a content-based hyperedge for each viewport.
Bayesian Graph Convolutional Network for Traffic Prediction
Under this framework, the graph structure is viewed as a random realization from a parametric generative model, and its posterior is inferred using the observed topology of the road network and traffic data.
Nonvolatile electric control of exciton complex in monolayer MoSe$_2$ with two dimensional ferroelectric CuInP$_2$S$_6$
With the saturated ferroelectric polarization of CIPS, electron-doped or hole-doped MoSe$_2$ is realized in a single device with a large carrier density tunability up to $5\times 10^{12}$cm$^{-2}$.
Materials Science
Bayesian Spatio-Temporal Graph Convolutional Network for Traffic Forecasting
The graph structure in our network is learned from the physical topology of the road network and traffic data in an end-to-end manner, which discovers a more accurate description of the relationship among traffic flows.
Ranked #2 on
Traffic Prediction
on SZ-Taxi
Scene Segmentation with Dual Relation-aware Attention Network
We conduct extensive experiments to validate the effectiveness of our network and achieve new state-of-the-art segmentation performance on four challenging scene segmentation data sets, i. e., Cityscapes, ADE20K, PASCAL Context, and COCO Stuff data sets.
Ranked #8 on
Semantic Segmentation
on COCO-Stuff test
Residual Squeeze-and-Excitation Network for Fast Image Deraining
Image deraining is an important image processing task as rain streaks not only severely degrade the visual quality of images but also significantly affect the performance of high-level vision tasks.
Adaptive Context Network for Scene Parsing
Recent works attempt to improve scene parsing performance by exploring different levels of contexts, and typically train a well-designed convolutional network to exploit useful contexts across all pixels equally.
Ranked #72 on
Semantic Segmentation
on ADE20K val
Dual Attention Network for Scene Segmentation
Specifically, we append two types of attention modules on top of traditional dilated FCN, which model the semantic interdependencies in spatial and channel dimensions respectively.
Ranked #6 on
Semantic Segmentation
on Trans10K
Stacked Deconvolutional Network for Semantic Segmentation
In SDN, multiple shallow deconvolutional networks, which are called as SDN units, are stacked one by one to integrate contextual information and guarantee the fine recovery of localization information.
Ranked #4 on
Semantic Segmentation
on PASCAL VOC 2012 test
