Search Results for author:
Found 6 papers, 2 papers with code
SYNAuG: Exploiting Synthetic Data for Data Imbalance Problems
Data imbalance in training data often leads to biased predictions from trained models, which in turn causes ethical and social issues.
TextManiA: Enriching Visual Feature by Text-driven Manifold Augmentation
Given the hypothesis, TextManiA transfers pre-trained text representation obtained from a well-established large language encoder to a target visual feature space being learned.
ENInst: Enhancing Weakly-supervised Low-shot Instance Segmentation
We address a weakly-supervised low-shot instance segmentation, an annotation-efficient training method to deal with novel classes effectively.
HDR-Plenoxels: Self-Calibrating High Dynamic Range Radiance Fields
Our voxel-based volume rendering pipeline reconstructs HDR radiance fields with only multi-view LDR images taken from varying camera settings in an end-to-end manner and has a fast convergence speed.
FoxInst: A Frustratingly Simple Baseline for Weakly Few-shot Instance Segmentation
We propose the first weakly-supervised few-shot instance segmentation task and a frustratingly simple but strong baseline model, FoxInst.
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning
We show that pFedPara outperforms competing personalized FL methods with more than three times fewer parameters.
