Papers Prompt Learning
“Prompt Learning” 태그가 달린 논문 678편 · 필터 해제
Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering
Most existing Knowledge Graph Question Answering (KGQA) approaches are designed for a specific KG, such as Wikidata, DBpedia or Freebase. Due to the heterogeneity of the underlying graph schema, topology and assertions, …
Graph Question AnsweringKnowledge GraphsLarge Language ModelPrompt Learning+2IMDPrompter: Adapting SAM to Image Manipulation Detection by Cross-View Automated Prompt Learning
Using extensive training data from SA-1B, the Segment Anything Model (SAM) has demonstrated exceptional generalization and zero-shot capabilities, attracting widespread attention in areas such as medical image segmentati…
Image ManipulationImage Manipulation DetectionImage SegmentationMedical Image Segmentation+2CLIP-DQA: Blindly Evaluating Dehazed Images from Global and Local Perspectives Using CLIP
Blind dehazed image quality assessment (BDQA), which aims to accurately predict the visual quality of dehazed images without any reference information, is essential for the evaluation, comparison, and optimization of ima…
Image DehazingImage Quality AssessmentPrompt LearningTechnical report on label-informed logit redistribution for better domain generalization in low-shot classification with foundation models
Confidence calibration is an emerging challenge in real-world decision systems based on foundations models when used for downstream vision classification tasks. Due to various reasons exposed, logit scores on the CLIP he…
Domain GeneralizationPrompt LearningLow-rank Prompt Interaction for Continual Vision-Language Retrieval
Research on continual learning in multi-modal tasks has been receiving increasing attention. However, most existing work overlooks the explicit cross-modal and cross-task interactions. In this paper, we innovatively prop…
Continual LearningContrastive LearningPrompt LearningRetrievalPromptMono: Cross Prompting Attention for Self-Supervised Monocular Depth Estimation in Challenging Environments
Considerable efforts have been made to improve monocular depth estimation under ideal conditions. However, in challenging environments, monocular depth estimation still faces difficulties. In this paper, we introduce vis…
Depth EstimationMonocular Depth EstimationPrompt LearningSelf-Supervised LearningPrivacy-Preserving Personalized Federated Prompt Learning for Multimodal Large Language Models
Multimodal Large Language Models (LLMs) are pivotal in revolutionizing customer support and operations by integrating multiple modalities such as text, images, and audio. Federated Prompt Learning (FPL) is a recently pro…
Federated LearningPrivacy PreservingPrompt LearningEnhancing Biomedical Relation Extraction with Directionality
Biological relation networks contain rich information for understanding the biological mechanisms behind the relationship of entities such as genes, proteins, diseases, and chemicals. The vast growth of biomedical litera…
BenchmarkingDocument-level Relation ExtractionLanguage ModelingLanguage Modelling+4TeD-Loc: Text Distillation for Weakly Supervised Object Localization
Weakly supervised object localization (WSOL) using classification models trained with only image-class labels remains an important challenge in computer vision. Given their reliance on classification objectives, traditio…
ClassificationDenoisingMultiple Instance LearningObject Localization+2HierPromptLM: A Pure PLM-based Framework for Representation Learning on Heterogeneous Text-rich Networks
Representation learning on heterogeneous text-rich networks (HTRNs), which consist of multiple types of nodes and edges with each node associated with textual information, is essential for various real-world applications…
Link PredictionNode ClassificationPrompt LearningRepresentation LearningSeparated Inter/Intra-Modal Fusion Prompts for Compositional Zero-Shot Learning
Compositional Zero-Shot Learning (CZSL) aims to recognize subtle differences in meaning or the combination of states and objects through the use of known and unknown concepts during training. Existing methods either focu…
AttributeCompositional Zero-Shot LearningLanguage ModelingLanguage Modelling+3fabSAM: A Farmland Boundary Delineation Method Based on the Segment Anything Model
Delineating farmland boundaries is essential for agricultural management such as crop monitoring and agricultural census. Traditional methods using remote sensing imagery have been efficient but limited in generalisation…
DecoderPrompt LearningIDEA: Image Description Enhanced CLIP-Adapter
CLIP (Contrastive Language-Image Pre-training) has attained great success in pattern recognition and computer vision. Transferring CLIP to downstream tasks (e.g. zero- or few-shot classification) is a hot topic in multim…
Few-Shot Image Classificationimage-classificationImage ClassificationImage Description+1BMIP: Bi-directional Modality Interaction Prompt Learning for VLM
Vision-language models (VLMs) have exhibited remarkable generalization capabilities, and prompt learning for VLMs has attracted great attention for the ability to adapt pre-trained VLMs to specific downstream tasks. Howe…
Domain GeneralizationPrompt LearningPGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation
The Segment Anything Model (SAM) has demonstrated strong and versatile segmentation capabilities, along with intuitive prompt-based interactions. However, customizing SAM for medical image segmentation requires massive a…
Image SegmentationMedical Image SegmentationPrompt LearningSemantic SegmentationLanguage-Inspired Relation Transfer for Few-shot Class-Incremental Learning
Depicting novel classes with language descriptions by observing few-shot samples is inherent in human-learning systems. This lifelong learning capability helps to distinguish new knowledge from old ones through the incre…
class-incremental learningClass Incremental LearningContrastive LearningFew-Shot Class-Incremental Learning+4Hidden Entity Detection from GitHub Leveraging Large Language Models
Named entity recognition is an important task when constructing knowledge bases from unstructured data sources. Whereas entity detection methods mostly rely on extensive training data, Large Language Models (LLMs) have p…
Few-Shot Learningnamed-entity-recognitionNamed Entity RecognitionPrompt Learning+1KAnoCLIP: Zero-Shot Anomaly Detection through Knowledge-Driven Prompt Learning and Enhanced Cross-Modal Integration
Zero-shot anomaly detection (ZSAD) identifies anomalies without needing training samples from the target dataset, essential for scenarios with privacy concerns or limited data. Vision-language models like CLIP show poten…
Anomaly DetectionAnomaly SegmentationGeneral KnowledgeLarge Language Model+4A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models
Designing effective prompts is essential to guiding large language models (LLMs) toward desired responses. Automated prompt engineering aims to reduce reliance on manual effort by streamlining the design, refinement, and…
Prompt EngineeringPrompt LearningImproving GenIR Systems Based on User Feedback
In this chapter, we discuss how to improve the GenIR systems based on user feedback. Before describing the approaches, it is necessary to be aware that the concept of "user" has been extended in the interactions with the…
Continual LearningPrompt Learning