Search Results for author:
Found 9 papers, 8 papers with code
DimCL: Dimensional Contrastive Learning For Improving Self-Supervised Learning
Self-supervised learning (SSL) has gained remarkable success, for which contrastive learning (CL) plays a key role.
Improving Low-Resource Question Answering using Active Learning in Multiple Stages
Furthermore, they often yield very good performance but only in the domain they were trained on.
Scalable SoftGroup for 3D Instance Segmentation on Point Clouds
Furthermore, SoftGroup can be extended to perform object detection and panoptic segmentation with nontrivial improvements over existing methods.
Learning Imbalanced Datasets with Maximum Margin Loss
A learning algorithm referred to as Maximum Margin (MM) is proposed for considering the class-imbalance data learning issue: the trained model tends to predict the majority of classes rather than the minority ones.
SoftGroup for 3D Instance Segmentation on Point Clouds
The hard predictions are made when performing semantic segmentation such that each point is associated with a single class.
Ranked #3 on
3D Instance Segmentation
on STPLS3D
Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics Model
Developing an agent in reinforcement learning (RL) that is capable of performing complex control tasks directly from high-dimensional observation such as raw pixels is yet a challenge as efforts are made towards improving sample efficiency and generalization.
SCNet: Training Inference Sample Consistency for Instance Segmentation
This paper proposes an architecture referred to as Sample Consistency Network (SCNet) to ensure that the IoU distribution of the samples at training time is close to that at inference time.
Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution
This paper considers an architecture referred to as Cascade Region Proposal Network (Cascade RPN) for improving the region-proposal quality and detection performance by \textit{systematically} addressing the limitation of the conventional RPN that \textit{heuristically defines} the anchors and \textit{aligns} the features to the anchors.
Ranked #172 on
Object Detection
on COCO test-dev
Fast and Efficient Image Quality Enhancement via Desubpixel Convolutional Neural Networks
This paper considers a convolutional neural network for image quality enhancement referred to as the fast and efficient quality enhancement (FEQE) that can be trained for either image super-resolution or image enhancement to provide accurate yet visually pleasing images on mobile devices by addressing the following three main issues.
