3D scene graph generation (SGG) has been of high interest in computer vision. Although the accuracy of 3D SGG on coarse classification and single relation label has been gradually improved, the performance of existing...
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Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label di...
ISBN:
(纸本)9781713871088
Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods.
Recent years have seen the wide application of NLP models in crucial areas such as finance, medical treatment, and news media, raising concerns of the model robustness and vulnerabilities. In this paper, we propose a ...
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This study presents a computational methodology for developing a multi-ball system that automatically aligns during assembly processes. The contributions of this work are threefold: Firstly, a comprehensive error anal...
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Deep-learning-based NLP models are found to be vulnerable to word substitution perturbations. Before they are widely adopted, the fundamental issues of robustness need to be addressed. Along this line, we propose a fo...
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Optical Coherence Tomography(OCT)is very important in medicine and provide useful diagnostic *** retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular *** the existing retinal layer s...
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Optical Coherence Tomography(OCT)is very important in medicine and provide useful diagnostic *** retinal layer thicknesses plays a vital role in pathophysiologic factors of many ocular *** the existing retinal layer segmentation approaches,learning or deep learning-based methods belong to the ***,most of these techniques rely on manual-marked layers and the performances are limited due to the image *** order to overcome this limitation,we build a framework based on gray value curve matching,which uses depth learning to match the curve for semi-automatic segmentation of retinal layers from *** depth convolution network learns the column correspondence in the OCT image *** whole OCT image participates in the depth convolution neural network operation,compares the gray value of each column,and matches the gray value sequence of the transformation column and the next *** this algorithm,when a boundary point is manually specified,we can accurately segment the boundary between retinal *** experimental results obtained from a 54-subjects database of both normal healthy eyes and affected eyes demonstrate the superior performances of our approach.
Large energy consumption in data centers has become a challenging problem with the emergence of cloud computing and large scale data centers. In this paper, we present an architectural framework for thermal-aware reso...
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Large energy consumption in data centers has become a challenging problem with the emergence of cloud computing and large scale data centers. In this paper, we present an architectural framework for thermal-aware resource management while considering energy efficiency. The framework consists of a layered architecture and integrates a set of easy-to-use client tools and a thermal-aware task management middleware to schedule tasks based on thermal conditions within a cluster and among different data centers. As part of this paper we focus on the development of a thermal-aware task scheduling component for a single data center. This component is fundamental to our future activities, while considering to balance the temperature distribution in a single data center, thus implicitly minimizing energy cost in data centers.
A new based on Semi-supervised classification theory for SAR images in contourlet domain is proposed, in this paper. Attempting to get better and faster performance, the PSO algorithm (Particle swarm optimization algo...
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Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of syno...
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ISBN:
(数字)9798350386226
ISBN:
(纸本)9798350386233
Biomedical Named Entity Recognition (NER) is a crucial task in extracting information from biomedical texts. However, the diversity of professional terminology, semantic complexity, and the widespread presence of synonyms pose significant challenges. Traditional methods that rely on sequence labeling training datasets often struggle to handle these complexities. To address this, we introduce a novel framework for BioNER, termed DMNER, which leverages external knowledge and operates in two steps: entity boundary detection and entity category identification through matching. The core of DMNER is its second step, which determines the entity category by retrieving similar entities and their categories from a knowledge dictionary using semantic similarity matching. Our experiments on 10 biomedical datasets demonstrate that DMNER outperforms baselines across these tasks, proving its effectiveness and adaptability. DMNER is versatile and can be applied to various NER tasks, including supervised NER, distantly supervised NER, and NER on multiple datasets with disjoint label sets. The DMNER code is publicly available
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Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label di...
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