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Found 11 papers, 1 papers with code
FSViewFusion: Few-Shots View Generation of Novel Objects
Second, we establish that the concept of a view can be disentangled and transferred to a novel object irrespective of the original object's identify from which the views are learnt.
Task-Agnostic Robust Representation Learning
To tackle this problem, a great amount of research has been done to study the training procedure of a network to improve its robustness.
A Continuous Mapping For Augmentation Design
Automated data augmentation (ADA) techniques have played an important role in boosting the performance of deep models.
Generate, Segment and Refine: Towards Generic Manipulation Segmentation
The advent of image sharing platforms and the easy availability of advanced photo editing software have resulted in a large quantities of manipulated images being shared on the internet.
DCAN: Dual Channel-wise Alignment Networks for Unsupervised Scene Adaptation
In particular, given an image from the source domain and unlabeled samples from the target domain, the generator synthesizes new images on-the-fly to resemble samples from the target domain in appearance and the segmentation network further refines high-level features before predicting semantic maps, both of which leverage feature statistics of sampled images from the target domain.
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
In this work, we focus on adapting the representations learned by segmentation networks across synthetic and real domains.
Adaptive RNN Tree for Large-Scale Human Action Recognition
The RNNs in RNN-T are co-trained with the action category hierarchy, which determines the structure of RNN-T.
Ranked #107 on
Skeleton Based Action Recognition
on NTU RGB+D
Guided Perturbations: Self-Corrective Behavior in Convolutional Neural Networks
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
Regularizing deep networks using efficient layerwise adversarial training
In this paper, we present an efficient approach to perform adversarial training by perturbing intermediate layer activations and study the use of such perturbations as a regularizer during training.
Self corrective Perturbations for Semantic Segmentation and Classification
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performance in many computer vision problems.
A Reinforcement Learning Approach to the View Planning Problem
We present a Reinforcement Learning (RL) solution to the view planning problem (VPP), which generates a sequence of view points that are capable of sensing all accessible area of a given object represented as a 3D model.
