Diplomacy is one of the most sophisticated activities in human society, involving complex interactions among multiple parties that require skills in social reasoning, negotiation, and long-term strategic planning. Pre...
CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking *** inevitably influences the adaptability to changes in object *** filter b...
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CNN(convolutional neural network)based real time trackers usually do not carry out online network update in order to maintain rapid tracking *** inevitably influences the adaptability to changes in object *** filter based trackers can update the model parameters online in real *** this paper,we present an end-to-end lightweight network architecture,namely Discriminant Correlation Filter Network(DCFNet).A differentiable DCF(discriminant correlation filter)layer is incorporated into a Siamese network architecture in order to learn the convolutional features and the correlation filter *** correlation filter can be efficiently updated *** previous work,we introduced a joint scale-position space to the DCFNet,forming a scale DCFNet which carries out the predictions of object scale and position *** combine the scale DCFNet with the convolutional-deconvolutional network,learning both the high-level embedding space representations and the low-level fine-grained representations for *** adaptability of the fine-grained correlation analysis and the generalization capability of the semantic embedding are complementary for visual *** back-propagation is derived in the Fourier frequency domain throughout the entire work,preserving the efficiency of the *** evaluations on the OTB(Object Tracking Benchmark)and VOT(Visual Object Tracking Challenge)datasets demonstrate that the proposed trackers have fast speeds,while maintaining tracking accuracy.
Text detection in the wild is an important stage for recognizing and interpreting text present in images or videos captured in uncontrolled environments in the wild. It helps in various applications such as scene unde...
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We prove that image classifiers are fundamentally sensitive to small perturbations in their inputs. Specifically, we show that given some image space of n-by-n images, all but a tiny fraction of images in any image cl...
Two-stage recommender systems play a crucial role in efficiently identifying relevant items and personalizing recommendations from a vast array of options. This paper, based on an error decomposition framework, analyz...
Semantic edge detection (SED) is pivotal for the precise demarcation of object boundaries, yet it faces ongoing challenges due to the prevalence of low-quality labels in current methods. In this paper, we present a no...
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Dear *** letter presents a normalization mechanism to effectively fuse infrared and visible images in an encoder-decoder *** images are decomposed into source-invariant structure and sourcespecific detail ***,the info...
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Dear *** letter presents a normalization mechanism to effectively fuse infrared and visible images in an encoder-decoder *** images are decomposed into source-invariant structure and sourcespecific detail ***,the information of detail features is sufficiently incorporated into the structure features using this normalization mechanism in the decoder,which generates high-contrast fused images with highlighted targets and abundant texture *** and quantitative experiments on two challenging datasets demonstrate the superiority of our method over current stateof-the-art methods.
Meta-learning has been widely employed to tackle the cold-start problem in user modeling. Similar to a guidebook for a new traveler, meta-learning significantly affects decision-making for new users in crucial scenari...
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Adaptive Testing System (ATS) is a promising testing mode, extensively utilized in standardized tests like the GRE. It offers a personalized ability assessment by dynamically adjusting questions based on individual ab...
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Adaptive Testing System (ATS) is a promising testing mode, extensively utilized in standardized tests like the GRE. It offers a personalized ability assessment by dynamically adjusting questions based on individual ability levels. Compared to traditional exams, ATS can improve the accuracy of ability estimates while simultaneously reducing the number of questions required. Despite the diverse ATS testing formats, tailored to different adaptability requirements in various testing scenarios, there is a notable absence of a unified framework to model them. In this paper, we introduce a unified data-driven ATS framework that conceptualizes the various testing formats as a hierarchical test structure search problem. It can learn directly from data to solve optimal questions for each student, eliminating the need for manual test design. The proposed solution algorithm comes with theoretical guarantees for the estimation error and convergence. Empirical results show that our framework maintains assessment accuracy while reducing question count by 20% on average and improving training stability. Copyright 2024 by the author(s)
作者：
Mellal, NacimaSaighi, AsmaInstitute of Applied Science & Technology
University of Oum Research Laboratory of Artificial Intelligence And Autonomous Objects Dept. Networks And Telecommunication El Bouaghi Algeria University of Oum
Research Laboratory of Artificial Intelligence And Autonomous Objects Dept. Computer Science El Bouaghi Algeria
AI models and especially deep learning models have found applications across various medical domains. While many studies focus on using electronic health records (EHRs) data to train AI models, implementing the revers...
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