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Found 15 papers, 9 papers with code
Learning Fabric Manipulation in the Real World with Human Videos
A promising alternative is to learn fabric manipulation directly from watching humans perform the task.
Robust Graph Structure Learning via Multiple Statistical Tests
A natural way to construct a graph among images is to treat each image as a node and assign pairwise image similarities as weights to corresponding edges.
Ada-NETS: Face Clustering via Adaptive Neighbour Discovery in the Structure Space
In Ada-NETS, each face is transformed to a new structure space, obtaining robust features by considering face features of the neighbour images.
Interpolation variable rate image compression
Compression standards have been used to reduce the cost of image storage and transmission for decades.
Fine-Grained AutoAugmentation for Multi-Label Classification
Data augmentation is a commonly used approach to improving the generalization of deep learning models.
A Linkage-based Doubly Imbalanced Graph Learning Framework for Face Clustering
Although imbalance problem has been extensively studied, the impact of imbalanced data on GCN- based linkage prediction task is quite different, which would cause problems in two aspects: imbalanced linkage labels and biased graph representations.
Importance Weighted Adversarial Discriminative Transfer for Anomaly Detection
In this way, an obvious gap can be produced between the distributions of normal and abnormal data in the target domain, therefore enabling the anomaly detection in the domain.
Spatiotemporal Entropy Model is All You Need for Learned Video Compression
The framework of dominant learned video compression methods is usually composed of motion prediction modules as well as motion vector and residual image compression modules, suffering from its complex structure and error propagation problem.
Tracking the Untrackable
Although short-term fully occlusion happens rare in visual object tracking, most trackers will fail under these circumstances.
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
Moreover, we propose a new model architecture to perform depth-wise and layer-wise aggregations, which not only further improves the accuracy but also reduces the model size.
Ranked #5 on
Visual Object Tracking
on VOT2017/18
Adversarial Discriminative Sim-to-real Transfer of Visuo-motor Policies
Policies can be transferred to real environments with only 93 labelled and 186 unlabelled real images.
Tuning Modular Networks with Weighted Losses for Hand-Eye Coordination
This paper introduces an end-to-end fine-tuning method to improve hand-eye coordination in modular deep visuo-motor policies (modular networks) where each module is trained independently.
Modular Deep Q Networks for Sim-to-real Transfer of Visuo-motor Policies
While deep learning has had significant successes in computer vision thanks to the abundance of visual data, collecting sufficiently large real-world datasets for robot learning can be costly.
The ACRV Picking Benchmark (APB): A Robotic Shelf Picking Benchmark to Foster Reproducible Research
We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark (APB).
Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control
This paper introduces a machine learning based system for controlling a robotic manipulator with visual perception only.
