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Found 13 papers, 7 papers with code
DiffusionEdge: Diffusion Probabilistic Model for Crisp Edge Detection
With the recent success of the diffusion probabilistic model (DPM), we found it is especially suitable for accurate and crisp edge detection since the denoising process is directly applied to the original image size.
RayMVSNet++: Learning Ray-based 1D Implicit Fields for Accurate Multi-View Stereo
In particular, we propose RayMVSNet which learns sequential prediction of a 1D implicit field along each camera ray with the zero-crossing point indicating scene depth.
Delving into Crispness: Guided Label Refinement for Crisp Edge Detection
Learning-based edge detection usually suffers from predicting thick edges.
RARE: Robust Masked Graph Autoencoder
To address this issue, we propose a novel SGP method termed Robust mAsked gRaph autoEncoder (RARE) to improve the certainty in inferring masked data and the reliability of the self-supervision mechanism by further masking and reconstructing node samples in the high-order latent feature space.
NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Images
We study the problem of reconstructing 3D feature curves of an object from a set of calibrated multi-view images.
Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network
With the development of various applications, such as social networks and knowledge graphs, graph data has been ubiquitous in the real world.
STEdge: Self-training Edge Detection with Multi-layer Teaching and Regularization
In particular, we impose a consistency regularization which enforces the outputs from each of the multiple layers to be consistent for the input image and its perturbed counterpart.
Video Abnormal Event Detection by Learning to Complete Visual Cloze Tests
With each patch and the patch sequence of a STC compared to a visual "word" and "sentence" respectively, we deliberately erase a certain "word" (patch) to yield a VCT.
Deep Anomaly Discovery From Unlabeled Videos via Normality Advantage and Self-Paced Refinement
With this property, we propose Localization based Reconstruction (LBR) as a strong UVAD baseline and a solid foundation of our solution.
Deep Fusion Clustering Network
Specifically, in our network, an interdependency learning-based Structure and Attribute Information Fusion (SAIF) module is proposed to explicitly merge the representations learned by an autoencoder and a graph autoencoder for consensus representation learning.
Multi-View Spectral Clustering with High-Order Optimal Neighborhood Laplacian Matrix
Multi-view spectral clustering can effectively reveal the intrinsic cluster structure among data by performing clustering on the learned optimal embedding across views.
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video Events
To build such a visual cloze test, a certain patch of STC is erased to yield an incomplete event (IE).
Ranked #14 on
Anomaly Detection
on CUHK Avenue
Modeling Dense Cross-Modal Interactions for Joint Entity-Relation Extraction
The network is carefully constructed by stacking multiple attention units in depth to fully model dense interactions over token-label spaces, in which two basic attention units are proposed to explicitly capture fine-grained correlations across different modalities (e. g., token-to-token and labelto-token).
