Search Results for author:
Found 7 papers, 1 papers with code
Background Learnable Cascade for Zero-Shot Object Detection
The major contributions for BLC are as follows: (i) we propose a multi-stage cascade structure named Cascade Semantic R-CNN to progressively refine the alignment between visual and semantic of ZSD; (ii) we develop the semantic information flow structure and directly add it between each stage in Cascade Semantic RCNN to further improve the semantic feature learning; (iii) we propose the background learnable region proposal network (BLRPN) to learn an appropriate word vector for background class and use this learned vector in Cascade Semantic R CNN, this design makes \Background Learnable" and reduces the confusion between background and unseen classes.
Ranked #6 on
Zero-Shot Object Detection
on PASCAL VOC'07
Online Task Scheduling for Fog Computing with Multi-Resource Fairness
In fog computing systems, one key challenge is online task scheduling, i. e., to decide the resource allocation for tasks that are continuously generated from end devices.
Service Chain Composition with Failures in NFV Systems: A Game-Theoretic Perspective
In this paper, we formulate the problem of service chain composition in NFV systems with failures as a non-cooperative game.
History-Aware Online Cache Placement in Fog-Assisted IoT Systems: An Integration of Learning and Control
Due to uncertainties in practice such as unknown file popularities, cache placement scheme design is still an open problem with unresolved challenges: 1) how to maintain time-averaged storage costs under budgets, 2) how to incorporate online learning to aid cache placement to minimize performance loss (a. k. a.
Networking and Internet Architecture Signal Processing
Green Offloading in Fog-Assisted IoT Systems: An Online Perspective Integrating Learning and Control
In this paper, we formulate such a task offloading problem with unknown system dynamics as a combinatorial multi-armed bandit (CMAB) problem with long-term constraints on time-averaged energy consumptions.
EasiCS: the objective and fine-grained classification method of cervical spondylosis dysfunction
The precise diagnosis is of great significance in developing precise treatment plans to restore neck function and reduce the burden posed by the cervical spondylosis (CS).
EasiCSDeep: A deep learning model for Cervical Spondylosis Identification using surface electromyography signal
In this paper, we present an intelligent method based on the deep learning to identify CS, using the surface electromyography (sEMG) signal.
