Papers Multi-Label Image Classification
“Multi-Label Image Classification” 태그가 달린 논문 131편 · 필터 해제
Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook
Multi-label image classification (MLIC), a fundamental task in computer vision, focuses on identifying multiple objects or concepts within an image, underpinning numerous read-world applications, such as autonomous drivi…
Multi-Label Image ClassificationAutonomous DrivingMAPLE: Multi-Path Adaptive Propagation with Level-Aware Embeddings for Hierarchical Multi-Label Image Classification
Hierarchical multi-label classification (HMLC) is essential for modeling structured label dependencies in remote sensing. Yet existing approaches struggle in multi-path settings, where images may activate multiple taxono…
Hierarchical Multi-label ClassificationMulti-Label Image ClassificationHELM: Hierarchical and Explicit Label Modeling with Graph Learning for Multi-Label Image Classification
Hierarchical multi-label classification (HMLC) is essential for modeling complex label dependencies in remote sensing. Existing methods, however, struggle with multi-path hierarchies where instances belong to multiple br…
Hierarchical Multi-label ClassificationMulti-Label Image ClassificationGraph LearningMAT-Agent: Adaptive Multi-Agent Training Optimization
Multi-label image classification demands adaptive training strategies to navigate complex, evolving visual-semantic landscapes, yet conventional methods rely on static configurations that falter in dynamic settings. We p…
Multi-Label Image ClassificationDomain GeneralizationData AugmentationLarge Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiograph…
Multi-Label Image ClassificationMulti-Label ClassificationDual-View Alignment Learning with Hierarchical-Prompt for Class-Imbalance Multi-Label Classification
Real-world datasets often exhibit class imbalance across multiple categories, manifesting as long-tailed distributions and few-shot scenarios. This is especially challenging in Class-Imbalanced Multi-Label Image Classifi…
Multi-Label Image ClassificationFew-Shot Image ClassificationMulti-Label ClassificationObject RecognitionSemantic-Aware Representation Learning via Conditional Transport for Multi-Label Image Classification
Multi-label image classification is a critical task in machine learning that aims to accurately assign multiple labels to a single image. While existing methods often utilize attention mechanisms or graph convolutional n…
Multi-Label Image ClassificationRepresentation LearningDomain Adaptation for Multi-label Image Classification: a Discriminator-free Approach
This paper introduces a discriminator-free adversarial-based approach termed DDA-MLIC for Unsupervised Domain Adaptation (UDA) in the context of Multi-Label Image Classification (MLIC). While recent efforts have explored…
Domain Adaptationimage-classificationImage ClassificationMulti-Label Image Classification+1Correlative and Discriminative Label Grouping for Multi-Label Visual Prompt Tuning
Modeling label correlations has always played a pivotal role in multi-label image classification (MLC), attracting significant attention from researchers. However, recent studies have overemphasized co-occurrence rel…
image-classificationImage ClassificationMixture-of-ExpertsMulti-Label Image Classification+1Multi-label Classification using Deep Multi-order Context-aware Kernel Networks
Multi-label classification is a challenging task in pattern recognition. Many deep learning methods have been proposed and largely enhanced classification performance. However, most of the existing sophisticated methods …
Classificationimage-classificationImage ClassificationMulti-Label Classification+2When the Small-Loss Trick is Not Enough: Multi-Label Image Classification with Noisy Labels Applied to CCTV Sewer Inspections
The maintenance of sewerage networks, with their millions of kilometers of pipe, heavily relies on efficient Closed-Circuit Television (CCTV) inspections. Many promising approaches based on multi-label image classificati…
image-classificationImage ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+1Category-Prompt Refined Feature Learning for Long-Tailed Multi-Label Image Classification
Real-world data consistently exhibits a long-tailed distribution, often spanning multiple categories. This complexity underscores the challenge of content comprehension, particularly in scenarios requiring Long-Tailed Mu…
image-classificationImage ClassificationMulti-Label Image ClassificationObject RecognitionHSVLT: Hierarchical Scale-Aware Vision-Language Transformer for Multi-Label Image Classification
The task of multi-label image classification involves recognizing multiple objects within a single image. Considering both valuable semantic information contained in the labels and essential visual features presented in …
image-classificationImage ClassificationMulti-Label Image ClassificationMulti-Label Plant Species Classification with Self-Supervised Vision Transformers
We present a transfer learning approach using a self-supervised Vision Transformer (DINOv2) for the PlantCLEF 2024 competition, focusing on the multi-label plant species classification. Our method leverages both base and…
image-classificationImage ClassificationManagementMulti-Label Image Classification+1reBEN: Refined BigEarthNet Dataset for Remote Sensing Image Analysis
This paper presents refined BigEarthNet (reBEN) that is a large-scale, multi-modal remote sensing dataset constructed to support deep learning (DL) studies for remote sensing image analysis. The reBEN dataset consists of…
image-classificationImage ClassificationMulti-Label Image ClassificationResampled Datasets Are Not Enough: Mitigating Societal Bias Beyond Single Attributes
We tackle societal bias in image-text datasets by removing spurious correlations between protected groups and image attributes. Traditional methods only target labeled attributes, ignoring biases from unlabeled ones. Usi…
Image Captioningimage-classificationImage ClassificationMulti-Label Image ClassificationCombining Supervised Learning and Reinforcement Learning for Multi-Label Classification Tasks with Partial Labels
Traditional supervised learning heavily relies on human-annotated datasets, especially in data-hungry neural approaches. However, various tasks, especially multi-label tasks like document-level relation extraction, pose …
Document-level Relation Extractionimage-classificationImage ClassificationMulti-Label Classification+4LADI v2: Multi-label Dataset and Classifiers for Low-Altitude Disaster Imagery
ML-based computer vision models are promising tools for supporting emergency management operations following natural disasters. Arial photographs taken from small manned and unmanned aircraft can be available soon after …
ManagementMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Image ClassificationFree Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification
Multi-label image classification datasets are often partially labeled where many labels are missing, posing a significant challenge to training accurate deep classifiers. However, the powerful Mixup sample-mixing data au…
BenchmarkingData Augmentationimage-classificationImage Classification+2Reproducibility Study of CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
This report is a reproducibility study of the paper "CDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification" (Abdelfattah et al, ICCV 2023). Our report makes the following contributions: (1) We prov…
image-classificationImage ClassificationMulti-Label Image Classification