Papers Multi-Label Image Classification
“Multi-Label Image Classification” 태그가 달린 논문 131편 · 필터 해제
ROCOv2: Radiology Objects in COntext Version 2, an Updated Multimodal Image Dataset
Automated medical image analysis systems often require large amounts of training data with high quality labels, which are difficult and time consuming to generate. This paper introduces Radiology Object in COntext versio…
image-classificationImage ClassificationMedical Image AnalysisMulti-Label Image Classification+1FolkTalent: Enhancing Classification and Tagging of Indian Folk Paintings
Indian folk paintings have a rich mosaic of symbols, colors, textures, and stories making them an invaluable repository of cultural legacy. The paper presents a novel approach to classifying these paintings into distinct…
image-classificationImage ClassificationMulti-Label Image ClassificationThe impact of Compositionality in Zero-shot Multi-label action recognition for Object-based tasks
Addressing multi-label action recognition in videos represents a significant challenge for robotic applications in dynamic environments, especially when the robot is required to cooperate with humans in tasks that involv…
Action RecognitionAction Recognition In Videosimage-classificationImage Classification+2TAI++: Text as Image for Multi-Label Image Classification by Co-Learning Transferable Prompt
The recent introduction of prompt tuning based on pre-trained vision-language models has dramatically improved the performance of multi-label image classification. However, some existing strategies that have been explore…
Diversityimage-classificationImage ClassificationMulti-Label Image Classification+1Multi-Label Continual Learning for the Medical Domain: A Novel Benchmark
Despite the critical importance of the medical domain in Deep Learning, most of the research in this area solely focuses on training models in static environments. It is only in recent years that research has begun to ad…
class-incremental learningClass Incremental LearningContinual LearningImage Classification+5Counterfactual Reasoning for Multi-Label Image Classification via Patching-Based Training
The key to multi-label image classification (MLC) is to improve model performance by leveraging label correlations. Unfortunately, it has been shown that overemphasizing co-occurrence relationships can cause the overfitt…
Causal InferencecounterfactualCounterfactual Reasoningimage-classification+3Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images
The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervised image classification. In particular, c…
Data Augmentationimage-classificationImage ClassificationMulti-Label Image Classification+1Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation
Most existing weakly supervised semantic segmentation (WSSS) methods rely on Class Activation Mapping (CAM) to extract coarse class-specific localization maps using image-level labels. Prior works have commonly used an o…
Auxiliary Learningimage-classificationImage ClassificationMulti-Label Image Classification+6NOAH: Learning Pairwise Object Category Attentions for Image Classification
A modern deep neural network (DNN) for image classification tasks typically consists of two parts: a backbone for feature extraction, and a head for feature encoding and class predication. We observe that the head struct…
Classificationimage-classificationImage ClassificationMulti-Label Image Classification+1Category-wise Fine-Tuning: Resisting Incorrect Pseudo-Labels in Multi-Label Image Classification with Partial Labels
Large-scale image datasets are often partially labeled, where only a few categories' labels are known for each image. Assigning pseudo-labels to unknown labels to gain additional training signals has become prevalent for…
Benchmarkingimage-classificationImage ClassificationMulti-Label Image ClassificationProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification
Multi-label image classification presents a challenging task in many domains, including computer vision and medical imaging. Recent advancements have introduced graph-based and transformer-based methods to improve perfor…
Contrastive Learningimage-classificationImage ClassificationMulti-Label Image Classification+1Language-Guided Transformer for Federated Multi-Label Classification
Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private data. Most existing approaches of FL only c…
ClassificationFederated Learningimage-classificationImage Classification+4MS-Twins: Multi-Scale Deep Self-Attention Networks for Medical Image Segmentation
Although transformer is preferred in natural language processing, some studies has only been applied to the field of medical imaging in recent years. For its long-term dependency, the transformer is expected to contribut…
ClassificationImage ClassificationImage SegmentationMedical Image Segmentation+3Text as Image: Learning Transferable Adapter for Multi-Label Classification
Pre-trained vision-language models have notably accelerated progress of open-world concept recognition. Their impressive zero-shot ability has recently been transferred to multi-label image classification via prompt tuni…
image-classificationImage ClassificationInstruction FollowingMulti-Label Classification+3SpliceMix: A Cross-scale and Semantic Blending Augmentation Strategy for Multi-label Image Classification
Recently, Mix-style data augmentation methods (e.g., Mixup and CutMix) have shown promising performance in various visual tasks. However, these methods are primarily designed for single-label images, ignoring the conside…
Data Augmentationimage-classificationImage ClassificationMulti-Label Image ClassificationFederated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification
Federated learning (FL) enables the collaboration of multiple deep learning models to learn from decentralized data archives (i.e., clients) without accessing data on clients. Although FL offers ample opportunities in kn…
Federated Learningimage-classificationImage ClassificationMulti-Label Classification+2Feature Guided Masked Autoencoder for Self-supervised Learning in Remote Sensing
Self-supervised learning guided by masked image modelling, such as Masked AutoEncoder (MAE), has attracted wide attention for pretraining vision transformers in remote sensing. However, MAE tends to excessively focus on …
Image ClassificationMulti-Label Image ClassificationSelf-Supervised LearningConditional Consistency Regularization for Semi-Supervised Multi-label Image Classification
Consistency regularization has achieved great successes in Semi-Supervised Single-Label Image Classification (SS-SLC) with deep learning models, while few effort has been devoted to Semi-Supervised Multi-Label Image Clas…
Classificationimage-classificationImage ClassificationMulti-Label Image ClassificationCDUL: CLIP-Driven Unsupervised Learning for Multi-Label Image Classification
This paper presents a CLIP-based unsupervised learning method for annotation-free multi-label image classification, including three stages: initialization, training, and inference. At the initialization stage, we take fu…
Classificationimage-classificationImage ClassificationMulti-Label Image Classification+2Semantic-Aware Dual Contrastive Learning for Multi-label Image Classification
Extracting image semantics effectively and assigning corresponding labels to multiple objects or attributes for natural images is challenging due to the complex scene contents and confusing label dependencies. Recent wor…
Contrastive Learningimage-classificationImage ClassificationMulti-Label Image Classification+2