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Found 8 papers, 5 papers with code
Joint Search of Data Augmentation Policies and Network Architectures
In this paper, we propose a joint optimization method for data augmentation policies and network architectures to bring more automation to the design of training pipeline.
Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score
Motivated by the fact that detecting these rare relations can be critical in real-world applications, this paper introduces a novel integrated framework of classification and ranking to resolve the class imbalance problem in scene graph parsing.
Chainer: A Deep Learning Framework for Accelerating the Research Cycle
Software frameworks for neural networks play a key role in the development and application of deep learning methods.
Train Sparsely, Generate Densely: Memory-efficient Unsupervised Training of High-resolution Temporal GAN
Training of Generative Adversarial Network (GAN) on a video dataset is a challenge because of the sheer size of the dataset and the complexity of each observation.
Minimizing Supervision for Free-space Segmentation
Our work demonstrates the potential for performing free-space segmentation without tedious and costly manual annotation, which will be important for adapting autonomous driving systems to different types of vehicles and environments
ChainerCV: a Library for Deep Learning in Computer Vision
Despite significant progress of deep learning in the field of computer vision, there has not been a software library that covers these methods in a unifying manner.
Ranked #200 on
Object Detection
on COCO test-dev
Distantly Supervised Road Segmentation
We present an approach for road segmentation that only requires image-level annotations at training time.
Temporal Generative Adversarial Nets with Singular Value Clipping
In this paper, we propose a generative model, Temporal Generative Adversarial Nets (TGAN), which can learn a semantic representation of unlabeled videos, and is capable of generating videos.
