1,180 research outputs found
Multi-Task Domain Adaptation for Deep Learning of Instance Grasping from Simulation
Learning-based approaches to robotic manipulation are limited by the
scalability of data collection and accessibility of labels. In this paper, we
present a multi-task domain adaptation framework for instance grasping in
cluttered scenes by utilizing simulated robot experiments. Our neural network
takes monocular RGB images and the instance segmentation mask of a specified
target object as inputs, and predicts the probability of successfully grasping
the specified object for each candidate motor command. The proposed transfer
learning framework trains a model for instance grasping in simulation and uses
a domain-adversarial loss to transfer the trained model to real robots using
indiscriminate grasping data, which is available both in simulation and the
real world. We evaluate our model in real-world robot experiments, comparing it
with alternative model architectures as well as an indiscriminate grasping
baseline.Comment: ICRA 201
Multi-Label Zero-Shot Learning with Structured Knowledge Graphs
In this paper, we propose a novel deep learning architecture for multi-label
zero-shot learning (ML-ZSL), which is able to predict multiple unseen class
labels for each input instance. Inspired by the way humans utilize semantic
knowledge between objects of interests, we propose a framework that
incorporates knowledge graphs for describing the relationships between multiple
labels. Our model learns an information propagation mechanism from the semantic
label space, which can be applied to model the interdependencies between seen
and unseen class labels. With such investigation of structured knowledge graphs
for visual reasoning, we show that our model can be applied for solving
multi-label classification and ML-ZSL tasks. Compared to state-of-the-art
approaches, comparable or improved performances can be achieved by our method.Comment: CVPR 201
Adaptive web service selection based on data type matching for dynamic web service composition
Although there are many web services provided for access in World Wide Web (WWW), some services are not available at all times.It is very important to ensure all services are available when a service composition takes place.A web service that meets the requirements of the workflow but does not match the data type will still cause a failure in composition.To address this concern, we propose an adaptive web service selection method which is able to replace a current web service which has been used for composition but fails during execution time.The proposed algorithm will select
the most appropriate web service based on web service discovery engine recommendation and match the requirement based on WSDL description.
Upon matching the requirements of the workflow, the selected web service will be matched according to the input and output data type. The goal of this paper is to ensure every web service that meets the requirements of the workflow does not get rejected when the data type does not fulfill the matching criteria
Webs: A web accessibility barrier severity metric
A novel metric for quantitatively measuring the severity of websites barriers that limit the accessibility for disabled people is proposed. The metric is based on the Web Content Accessibility Guidelines (WCAG 2.0), which is the most adopted voluntary web accessibility standard internationally that can be tested automatically. The proposed metric is intended to rank the accessibility barriers based on their severity rather than the total conformance to priority levels.Our metric meets the requirements as a measurement for scientific research. An experiment is conducted to assess the results of our metric and to reveal the commonplace violations that persist in websites and affect disabled people interacting with the web
Resource Scheduling Strategy for Performance Optimization Based on Heterogeneous CPU-GPU Platform
In recent years, with the development of processor architecture, heterogeneous processors including Center processing unit (CPU) and Graphics processing unit (GPU) have become the mainstream. However, due to the differences of heterogeneous core, the heterogeneous system is now facing many problems that need to be solved. In order to solve these problems, this paper try to focus on the utilization and efficiency of heterogeneous core and design some reasonable resource scheduling strategies. To improve the performance of the system, this paper proposes a combination strategy for a single task and a multi-task scheduling strategy for multiple tasks. The combination strategy consists of two sub-strategies, the first strategy improves the execution efficiency of tasks on the GPU by changing the thread organization structure. The second focuses on the working state of the efficient core and develops more reasonable workload balancing schemes to improve resource utilization of heterogeneous systems. The multi-task scheduling strategy obtains the execution efficiency of heterogeneous cores and global task information through the processing of task samples. Based on this information, an improved ant colony algorithm is used to quickly obtain a reasonable task allocation scheme, which fully utilizes the characteristics of heterogeneous cores. The experimental results show that the combination strategy reduces task execution time by 29.13% on average. In the case of processing multiple tasks, the multi-task scheduling strategy reduces the execution time by up to 23.38% based on the combined strategy. Both strategies can make better use of the resources of heterogeneous systems and significantly reduce the execution time of tasks on heterogeneous systems
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