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Found 12 papers, 9 papers with code
Contrastive Regularization for Semi-Supervised Learning
Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high performance.
The Medical Segmentation Decathlon
Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem.
Spatially Consistent Representation Learning
While these contrastive methods mainly focus on generating invariant global representations at the image-level under semantic-preserving transformations, they are prone to overlook spatial consistency of local representations and therefore have a limitation in pretraining for localization tasks such as object detection and instance segmentation.
ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision
Vision-and-Language Pre-training (VLP) has improved performance on various joint vision-and-language downstream tasks.
Ranked #2 on
Image Retrieval
on PhotoChat
Learning Loss for Test-Time Augmentation
Data augmentation has been actively studied for robust neural networks.
AutoCLINT: The Winning Method in AutoCV Challenge 2019
NeurIPS 2019 AutoDL challenge is a series of six automated machine learning competitions.
torchgpipe: On-the-fly Pipeline Parallelism for Training Giant Models
We design and implement a ready-to-use library in PyTorch for performing micro-batch pipeline parallelism with checkpointing proposed by GPipe (Huang et al., 2019).
Spatially Attentive Output Layer for Image Classification
In this paper, we propose a novel spatial output layer on top of the existing convolutional feature maps to explicitly exploit the location-specific output information.
VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results
Results of 33 object detection algorithms are presented.
Scalable Neural Architecture Search for 3D Medical Image Segmentation
In this paper, a neural architecture search (NAS) framework is proposed for 3D medical image segmentation, to automatically optimize a neural architecture from a large design space.
Fast AutoAugment
Data augmentation is an essential technique for improving generalization ability of deep learning models.
Ranked #3 on
Image Classification
on SVHN
The Liver Tumor Segmentation Benchmark (LiTS)
In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018.
