Papers Multi-Label Image Recognition
“Multi-Label Image Recognition” 태그가 달린 논문 37편 · 필터 해제
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning
Benefited from image-text contrastive learning, pre-trained vision-language models, e.g., CLIP, allow to direct leverage texts as images (TaI) for parameter-efficient fine-tuning (PEFT). While CLIP is capable of making i…
Contrastive LearningImage GenerationMulti-Label Image Recognitionparameter-efficient fine-tuning+2Task Singular Vectors: Reducing Task Interference in Model Merging
Task Arithmetic has emerged as a simple yet effective method to merge models without additional training. However, by treating entire networks as flat parameter vectors, it overlooks key structural information and is sus…
ClassificationImage ClassificationMulti-Label Image RecognitionTask ArithmeticSSPA: Split-and-Synthesize Prompting with Gated Alignments for Multi-Label Image Recognition
Multi-label image recognition is a fundamental task in computer vision. Recently, Vision-Language Models (VLMs) have made notable advancements in this area. However, previous methods fail to effectively leverage the rich…
In-Context LearningMulti-Label Image RecognitionText-Region Matching for Multi-Label Image Recognition with Missing Labels
Recently, large-scale visual language pre-trained (VLP) models have demonstrated impressive performance across various downstream tasks. Motivated by these advancements, pioneering efforts have emerged in multi-label ima…
Contrastive LearningMissing LabelsMulti-Label Image RecognitionPseudo LabelThe Solution for Language-Enhanced Image New Category Discovery
Treating texts as images, combining prompts with textual labels for prompt tuning, and leveraging the alignment properties of CLIP have been successfully applied in zero-shot multi-label image recognition. Nonetheless, r…
Contrastive LearningDiversityMulti-Label Image RecognitionSentenceData-free Multi-label Image Recognition via LLM-powered Prompt Tuning
This paper proposes a novel framework for multi-label image recognition without any training data, called data-free framework, which uses knowledge of pre-trained Large Language Model (LLM) to learn prompts to adapt pret…
Language ModelingLanguage ModellingLarge Language ModelMulti-Label Image Recognition+1PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition
Multi-label image recognition is a fundamental task in computer vision. Recently, vision-language models have made notable advancements in this area. However, previous methods often failed to effectively leverage the ric…
Multi-Label Image RecognitionRepresentation LearningLearning to recognize occluded and small objects with partial inputs
Recognizing multiple objects in an image is challenging due to occlusions, and becomes even more so when the objects are small. While promising, existing multi-label image recognition models do not explicitly learn conte…
Multi-Label Image RecognitionGKGNet: Group K-Nearest Neighbor based Graph Convolutional Network for Multi-Label Image Recognition
Multi-Label Image Recognition (MLIR) is a challenging task that aims to predict multiple object labels in a single image while modeling the complex relationships between labels and image regions. Although convolutional n…
graph constructionMulti-Label ClassificationMulti-Label Image RecognitionDualCoOp++: Fast and Effective Adaptation to Multi-Label Recognition with Limited Annotations
Multi-label image recognition in the low-label regime is a task of great challenge and practical significance. Previous works have focused on learning the alignment between textual and visual spaces to compensate for lim…
Multi-Label Image RecognitionSemantic Contrastive Bootstrapping for Single-positive Multi-label Recognition
Learning multi-label image recognition with incomplete annotation is gaining popularity due to its superior performance and significant labor savings when compared to training with fully labeled datasets. Existing litera…
Contrastive LearningMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label Image RecognitionSemantic-Aware Graph Matching Mechanism for Multi-Label Image Recognition
Multi-label image recognition aims to predict a set of labels that present in an image. The key to deal with such problem is to mine the associations between image contents and labels, and further obtain the correct assi…
Few-Shot LearningGraph MatchingMulti-Label Image RecognitionMulti-Label Continual Learning using Augmented Graph Convolutional Network
Multi-Label Continual Learning (MLCL) builds a class-incremental framework in a sequential multi-label image recognition data stream. The critical challenges of MLCL are the construction of label relationships on past-mi…
Continual LearningMulti-Label Image RecognitionTexts as Images in Prompt Tuning for Multi-Label Image Recognition
Prompt tuning has been employed as an efficient way to adapt large vision-language pre-trained models (e.g. CLIP) to various downstream tasks in data-limited or label-limited settings. Nonetheless, visual data (e.g., ima…
Contrastive LearningMulti-Label Image RecognitionMulti-label Image Recognition with Partial LabelsCategory-Adaptive Label Discovery and Noise Rejection for Multi-label Image Recognition with Partial Positive Labels
As a promising solution of reducing annotation cost, training multi-label models with partial positive labels (MLR-PPL), in which merely few positive labels are known while other are missing, attracts increasing attentio…
Multi-Label Image RecognitionSemantic SimilaritySemantic Textual SimilarityDual-Perspective Semantic-Aware Representation Blending for Multi-Label Image Recognition with Partial Labels
Despite achieving impressive progress, current multi-label image recognition (MLR) algorithms heavily depend on large-scale datasets with complete labels, making collecting large-scale datasets extremely time-consuming a…
image-classificationImage ClassificationMulti-Label Image RecognitionMulti-label Image Recognition with Partial LabelsHeterogeneous Semantic Transfer for Multi-label Recognition with Partial Labels
Multi-label image recognition with partial labels (MLR-PL), in which some labels are known while others are unknown for each image, may greatly reduce the cost of annotation and thus facilitate large-scale MLR. We find t…
Multi-Label Image RecognitionMulti-label Image Recognition with Partial LabelsDiverse Instance Discovery: Vision-Transformer for Instance-Aware Multi-Label Image Recognition
Previous works on multi-label image recognition (MLIR) usually use CNNs as a starting point for research. In this paper, we take pure Vision Transformer (ViT) as the research base and make full use of the advantages of T…
Multi-Label Image RecognitionObject LocalizationWeakly-Supervised Object LocalizationSemantic Representation and Dependency Learning for Multi-Label Image Recognition
Recently many multi-label image recognition (MLR) works have made significant progress by introducing pre-trained object detection models to generate lots of proposals or utilizing statistical label co-occurrence enhance…
Multi-Label Image RecognitionObjectobject-detectionObject DetectionAGCN: Augmented Graph Convolutional Network for Lifelong Multi-label Image Recognition
The Lifelong Multi-Label (LML) image recognition builds an online class-incremental classifier in a sequential multi-label image recognition data stream. The key challenges of LML image recognition are the construction o…
Multi-Label Image Recognition