Search
Search Results
-
Tree-based indexing technique for efficient and real-time label retrieval in the object tracking system
In real-time object tracking systems, it is essential to assign a unique identifier or label to each tracked object to distinguish it from other...
-
Domain and label efficient approach for diabetic retinopathy severity detection
Progress in medical imaging models using supervised learning has reached closer to clinical-level performance of doctors. However, labeling huge...
-
A principal label space transformation and ridge regression-based hybrid gorilla troops optimization and jellyfish search algorithm for multi-label classification
Classification as an essential part of Machine Learning and Data Mining has significant roles in engineering, medicine, agriculture, military, etc....
-
LA3: Efficient Label-Aware AutoAugment
Automated augmentation is an emerging and effective technique to search for data augmentation policies to improve generalizability of deep neural...
-
Label-Efficient Cross-Resolution Polyp Segmentation in Colonoscopy
Pixel-wise annotation is crucial but expensive for many deep-learning based application, especially for high-resolution (HR) image segmentation....
-
Granular ball-based label enhancement for dimensionality reduction in multi-label data
AbstractAs an important preprocessing procedure, dimensionality reduction for multi-label learning is an effective way to solve the challenge caused...
-
Label-representative graph convolutional network for multi-label text classification
Multi-label text classification (MLTC) is the task that assigns each document to the most relevant subset of class labels. Previous works usually...
-
LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds
Semantic segmentation of LiDAR point clouds is an important task in autonomous driving. However, training deep models via conventional supervised...
-
MELEP: A Novel Predictive Measure of Transferability in Multi-label ECG Diagnosis
In practical electrocardiography (ECG) interpretation, the scarcity of well-annotated data is a common challenge. Transfer learning techniques are...
-
Optimal Transport for Label-Efficient Visible-Infrared Person Re-Identification
Visible-infrared person re-identification (VI-ReID) has been a key enabler for night intelligent monitoring system. However, the extensive laboring...
-
Constrained clustering with weak label prior
Clustering is widely exploited in data mining. It has been proved that embedding weak label prior into clustering is effective to promote its...
-
Multi-label neural architecture search for chest radiography image classification
Chest radiography remain the global standard for diagnosing pulmonary diseases. Despite numerous research efforts, medical professionals still face...
-
HLC: hierarchically-aware label correlation for hierarchical text classification
AbstractHierarchical Text Classification (HTC) leverages the hierarchical structure of labels to enhance text categorization. Existing methods use a...
-
DALLMi: Domain Adaption for LLM-Based Multi-label Classifier
Large language models (LLMs) increasingly serve as the backbone for classifying text associated with distinct domains and simultaneously several...
-
Visual transductive learning via iterative label correction
Unsupervised domain adaptation (UDA) aims to transfer knowledge across domains when there is no labeled data available in the target domain. In this...
-
Bi-directional matrix completion for highly incomplete multi-label learning via co-embedding predictive side information
Motivated by real-world applications such as recommendation systems and social networks where only “likes” or “friendships” are observed, we consider...
-
Concise and interpretable multi-label rule sets
Multi-label classification is becoming increasingly ubiquitous, but not much attention has been paid to interpretability. In this paper, we develop a...
-
Self-training involving semantic-space finetuning for semi-supervised multi-label document classification
Self-training is an effective solution for semi-supervised learning, in which both labeled and unlabeled data are leveraged for training. However,...
-
Fast block-wise partitioning for extreme multi-label classification
Extreme multi-label classification aims to learn a classifier that annotates an instance with a relevant subset of labels from an extremely large...
-
Domain-invariant feature learning with label information integration for cross-domain classification
Traditional methods for unsupervised cross-domain classification learn a common low-dimensional subspace using images from a well-labeled source...
