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Found 9 papers, 3 papers with code
Distance-Normalized Unified Representation for Monocular 3D Object Detection
Monocular 3D object detection plays an important role in autonomous driving and still remains challenging.
MAPConNet: Self-supervised 3D Pose Transfer with Mesh and Point Contrastive Learning
Unsupervised methods have been proposed for graph convolutional models but they require ground truth correspondence between the source and target inputs.
eBPF-based Working Set Size Estimation in Memory Management
Working set size estimation (WSS) is of great significance to improve the efficiency of program executing and memory arrangement in modern operating systems.
Semi-Supervised Object Detection with Object-wise Contrastive Learning and Regression Uncertainty
Unlike existing suboptimal methods, we propose a two-step pseudo-label filtering for the classification and regression heads in a teacher-student framework.
Ranked #14 on
Semi-Supervised Object Detection
on COCO 1% labeled data
SeCGAN: Parallel Conditional Generative Adversarial Networks for Face Editing via Semantic Consistency
Whilst both branches are required during training, the RGB branch is our primary network and the semantic branch is not needed for inference.
NEUer at SemEval-2021 Task 4: Complete Summary Representation by Filling Answers into Question for Matching Reading Comprehension
SemEval task 4 aims to find a proper option from multiple candidates to resolve the task of machine reading comprehension.
Learning Feature Aggregation for Deep 3D Morphable Models
3D morphable models are widely used for the shape representation of an object class in computer vision and graphics applications.
Geometry-based Distance Decomposition for Monocular 3D Object Detection
The experimental results show that our method achieves the state-of-the-art performance on the monocular 3D Object Detection and Birds Eye View tasks of the KITTI dataset, and can generalize to images with different camera intrinsics.
Ranked #15 on
Monocular 3D Object Detection
on KITTI Cars Moderate
Deep Hashing via Discrepancy Minimization
This paper presents a discrepancy minimizing model to address the discrete optimization problem in hashing learning.
