Search Results for author:
Found 7 papers, 1 papers with code
Modeling Multi-Granularity Context Information Flow for Pavement Crack Detection
In this paper, we address these problems from a view that utilizes contexts of the cracks and propose an end-to-end deep learning method to model the context information flow.
Pixel-Wise Symbol Spotting via Progressive Points Location for Parsing CAD Images
Based on the keypoints detection, we propose a symbol grouping method to redraw the rectangle symbols in CAD images.
A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification
Semi-supervised learning (SSL) is a practical challenge in computer vision.
Effective Gradient Sample Size via Variation Estimation for Accelerating Sharpness aware Minimization
Concretely, we discover that the gradient of SAM is a combination of the gradient of SGD and the Projection of the Second-order gradient matrix onto the First-order gradient (PSF).
Push Quantization-Aware Training Toward Full Precision Performances via Consistency Regularization
Existing Quantization-Aware Training (QAT) methods intensively depend on the complete labeled dataset or knowledge distillation to guarantee the performances toward Full Precision (FP) accuracies.
Modeling the Uncertainty with Maximum Discrepant Students for Semi-supervised 2D Pose Estimation
Semi-supervised pose estimation is a practically challenging task for computer vision.
Generating Unbiased Pseudo-labels via a Theoretically Guaranteed Chebyshev Constraint to Unify Semi-supervised Classification and Regression
Both semi-supervised classification and regression are practically challenging tasks for computer vision.
