289 research outputs found
A novel approach to reconstruction based saliency detection via convolutional neural network stacked with auto-encoder
Shanghai VLBI Correlator
This report summarizes the activities of the Shanghai VLBI Correlator during 2012
Application of numerical inverse method in calculation of composition-dependent interdiffusion coefficients in finite diffusion couples
The previously developed numerical inverse method was applied to determine the composition-dependent interdiffusion coefficients in single-phase finite diffusion couples. The numerical inverse method was first validated in a fictitious binary finite diffusion couple by pre-assuming four standard sets of interdiffusion coefficients. After that, the numerical inverse method was then adopted in a ternary Al-Cu-Ni finite diffusion couple. Based on the measured composition profiles, the ternary interdiffusion coefficients along the entire diffusion path of the target ternary diffusion couple were obtained by using the numerical inverse approach. The comprehensive comparisons between the computations and the experiments indicate that the numerical inverse method is also applicable to high-throughput determination of the composition-dependent interdiffusion coefficients in finite diffusion couples
Bipartite Consensus for a Class of Nonlinear Multi-agent Systems Under Switching Topologies:A Disturbance Observer-Based Approach
This paper considers the leader-following bipartite consensus for a class of nonlinear multi-agent systems (MASs) subject to exogenous disturbances under directed fixed and switching topologies, respectively. Firstly, two new output feedback control protocols involving signs of link weights are introduced based on relative output measurements of neighboring agents. In order to estimate the disturbances produced by an exogenous system, a disturbance observer-based approach is developed. Then, sufficient conditions for leader-following bipartite consensus with directed fixed topologies are derived. Furthermore, by assuming that each switching topology contains a directed spanning tree, it is proved that the leader-following bipartite consensus can be realized with the designed output feedback control protocol if the dwell time is larger than a non-negative threshold. Finally, numerical simulations inspired by a real-world DC motors are provided to illustrate the effectiveness of the proposed controllers
OpenGCD: Assisting Open World Recognition with Generalized Category Discovery
A desirable open world recognition (OWR) system requires performing three
tasks: (1) Open set recognition (OSR), i.e., classifying the known (classes
seen during training) and rejecting the unknown (unseennovel classes)
online; (2) Grouping and labeling these unknown as novel known classes; (3)
Incremental learning (IL), i.e., continual learning these novel classes and
retaining the memory of old classes. Ideally, all of these steps should be
automated. However, existing methods mostly assume that the second task is
completely done manually. To bridge this gap, we propose OpenGCD that combines
three key ideas to solve the above problems sequentially: (a) We score the
origin of instances (unknown or specifically known) based on the uncertainty of
the classifier's prediction; (b) For the first time, we introduce generalized
category discovery (GCD) techniques in OWR to assist humans in grouping
unlabeled data; (c) For the smooth execution of IL and GCD, we retain an equal
number of informative exemplars for each class with diversity as the goal.
Moreover, we present a new performance evaluation metric for GCD called
harmonic clustering accuracy. Experiments on two standard classification
benchmarks and a challenging dataset demonstrate that OpenGCD not only offers
excellent compatibility but also substantially outperforms other baselines.
Code: https://github.com/Fulin-Gao/OpenGCD
QR-CLIP: Introducing Explicit Open-World Knowledge for Location and Time Reasoning
Daily images may convey abstract meanings that require us to memorize and
infer profound information from them. To encourage such human-like reasoning,
in this work, we teach machines to predict where and when it was taken rather
than performing basic tasks like traditional segmentation or classification.
Inspired by Horn's QR theory, we designed a novel QR-CLIP model consisting of
two components: 1) the Quantity module first retrospects more open-world
knowledge as the candidate language inputs; 2) the Relevance module carefully
estimates vision and language cues and infers the location and time.
Experiments show our QR-CLIP's effectiveness, and it outperforms the previous
SOTA on each task by an average of about 10% and 130% relative lift in terms of
location and time reasoning. This study lays a technical foundation for
location and time reasoning and suggests that effectively introducing
open-world knowledge is one of the panaceas for the tasks.Comment: Technical Report. Github: https://github.com/Shi-Wm/QR-CLI
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