Search Results for author:
Found 9 papers, 2 papers with code
Scale-Equalizing Pyramid Convolution for Object Detection
Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution.
Ranked #76 on
Object Detection
on COCO test-dev
Group R-CNN for Weakly Semi-supervised Object Detection with Points
The core of this task is to train a point-to-box regressor on well-labeled images that can be used to predict credible bounding boxes for each point annotation.
CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies
The Conference on Computational Natural Language Learning (CoNLL) features a shared task, in which participants train and test their learning systems on the same data sets.
Scale Calibrated Training: Improving Generalization of Deep Networks via Scale-Specific Normalization
Experiment results show that SCT improves accuracy of single Resnet-50 on ImageNet by 1. 7% and 11. 5% accuracy when testing on image sizes of 224 and 128 respectively.
CrossMatch: Improving Semi-Supervised Object Detection via Multi-Scale Consistency
Inspired by the fact that teacher/student pseudo-labeling approaches result in a weak and sparse gradient signal due to the difficulty of confidence-thresholding, CrossMatch leverages \textit{multi-scale feature extraction} in object detection.
EnergyMatch: Energy-based Pseudo-Labeling for Semi-Supervised Learning
However, it has been shown that softmax-based confidence scores in deep networks can be arbitrarily high for samples far from the training data, and thus, the pseudo-labels for even high-confidence unlabeled samples may still be unreliable.
InPL: Pseudo-labeling the Inliers First for Imbalanced Semi-supervised Learning
Without relying on model confidence, we propose to measure whether an unlabeled sample is likely to be ``in-distribution''; i. e., close to the current training data.
Denoising and Selecting Pseudo-Heatmaps for Semi-Supervised Human Pose Estimation
First, we introduce a denoising scheme to generate reliable pseudo-heatmaps as targets for learning from unlabeled data.
Diversify, Don't Fine-Tune: Scaling Up Visual Recognition Training with Synthetic Images
We present a new framework leveraging off-the-shelf generative models to generate synthetic training images, addressing multiple challenges: class name ambiguity, lack of diversity in naive prompts, and domain shifts.
