Papers Prompt Learning
“Prompt Learning” 태그가 달린 논문 678편 · 필터 해제
Efficient Motion Prompt Learning for Robust Visual Tracking
Due to the challenges of processing temporal information, most trackers depend solely on visual discriminability and overlook the unique temporal coherence of video data. In this paper, we propose a lightweight and plug-…
DecoderPrompt LearningVisual TrackingPL-FGSA: A Prompt Learning Framework for Fine-Grained Sentiment Analysis Based on MindSpore
Fine-grained sentiment analysis (FGSA) aims to identify sentiment polarity toward specific aspects within a text, enabling more precise opinion mining in domains such as product reviews and social media. However, traditi…
Aspect ExtractionOpinion MiningPrompt LearningSentiment Analysis+2EpiLLM: Unlocking the Potential of Large Language Models in Epidemic Forecasting
Advanced epidemic forecasting is critical for enabling precision containment strategies, highlighting its strategic importance for public health security. While recent advances in Large Language Models (LLMs) have demons…
Prompt LearningReSW-VL: Representation Learning for Surgical Workflow Analysis Using Vision-Language Model
Surgical phase recognition from video is a technology that automatically classifies the progress of a surgical procedure and has a wide range of potential applications, including real-time surgical support, optimization …
Language ModelingLanguage ModellingPrompt LearningRepresentation Learning+2Incomplete In-context Learning
Large vision language models (LVLMs) achieve remarkable performance through Vision In-context Learning (VICL), a process that depends significantly on demonstrations retrieved from an extensive collection of annotated ex…
Binary ClassificationIn-Context LearningPrompt LearningRetrievalBiomed-DPT: Dual Modality Prompt Tuning for Biomedical Vision-Language Models
Prompt learning is one of the most effective paradigms for adapting pre-trained vision-language models (VLMs) to the biomedical image classification tasks in few shot scenarios. However, most of the current prompt learni…
Clinical KnowledgeDiagnosticimage-classificationImage Classification+3Learning Knowledge-based Prompts for Robust 3D Mask Presentation Attack Detection
3D mask presentation attack detection is crucial for protecting face recognition systems against the rising threat of 3D mask attacks. While most existing methods utilize multimodal features or remote photoplethysmograph…
Face RecognitionKnowledge GraphsPrompt LearningEnhancing Target-unspecific Tasks through a Features Matrix
Recent developments in prompt learning of large vision-language models have significantly improved performance in target-specific tasks. However, these prompt optimizing methods often struggle to tackle the target-unspec…
General KnowledgePrompt LearningDual Prompting for Diverse Count-level PET Denoising
The to-be-denoised positron emission tomography (PET) volumes are inherent with diverse count levels, which imposes challenges for a unified model to tackle varied cases. In this work, we resort to the recently flourishe…
DenoisingPrompt LearningBeyond Whole Dialogue Modeling: Contextual Disentanglement for Conversational Recommendation
Conversational recommender systems aim to provide personalized recommendations by analyzing and utilizing contextual information related to dialogue. However, existing methods typically model the dialogue context as a wh…
Conversational RecommendationcounterfactualCounterfactual InferenceDisentanglement+3FrogDogNet: Fourier frequency Retained visual prompt Output Guidance for Domain Generalization of CLIP in Remote Sensing
In recent years, large-scale vision-language models (VLMs) like CLIP have gained attention for their zero-shot inference using instructional text prompts. While these models excel in general computer vision, their potent…
Domain GeneralizationPrompt LearningScene ClassificationDistribution-aware Forgetting Compensation for Exemplar-Free Lifelong Person Re-identification
Lifelong Person Re-identification (LReID) suffers from a key challenge in preserving old knowledge while adapting to new information. The existing solutions include rehearsal-based and rehearsal-free methods to address t…
Exemplar-FreeKnowledge DistillationLifelong learningMixture-of-Experts+4Bayesian Principles Improve Prompt Learning In Vision-Language Models
Prompt learning is a popular fine-tuning method for vision-language models due to its efficiency. It requires a small number of additional learnable parameters while significantly enhancing performance on target tasks. H…
Prompt LearningCLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey
As machine learning evolves, domain generalization (DG) and domain adaptation (DA) have become crucial for enhancing model robustness across diverse environments. Contrastive Language-Image Pretraining (CLIP) plays a sig…
Computational EfficiencyDomain AdaptationDomain GeneralizationPrompt Learning+2Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning
Fine-tuning vision-language models (VLMs) with large amounts of unlabeled data has recently garnered significant interest. However, a key challenge remains the lack of high-quality pseudo-labeled data. Current pseudo-lab…
Prompt LearningPseudo LabelMSCRS: Multi-modal Semantic Graph Prompt Learning Framework for Conversational Recommender Systems
Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by interacting with users through conversations. Most existing studies of CRS focus on extracting user preferences from conversational…
Prompt LearningRecommendation SystemsResponse GenerationCrane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly Detections
Anomaly Detection (AD) involves identifying deviations from normal data distributions and is critical in fields such as medical diagnostics and industrial defect detection. Traditional AD methods typically require the av…
Anomaly DetectionAnomaly LocalizationDefect DetectionPrompt Learning+2Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration
All-in-one image restoration, addressing diverse degradation types with a unified model, presents significant challenges in designing task-specific prompts that effectively guide restoration across multiple degradation s…
AllImage RestorationPrompt LearningUnified Image RestorationKAN-SAM: Kolmogorov-Arnold Network Guided Segment Anything Model for RGB-T Salient Object Detection
Existing RGB-thermal salient object detection (RGB-T SOD) methods aim to identify visually significant objects by leveraging both RGB and thermal modalities to enable robust performance in complex scenarios, but they oft…
Diversityobject-detectionObject DetectionPrompt Learning+2LightPROF: A Lightweight Reasoning Framework for Large Language Model on Knowledge Graph
Large Language Models (LLMs) have impressive capabilities in text understanding and zero-shot reasoning. However, delays in knowledge updates may cause them to reason incorrectly or produce harmful results. Knowledge Gra…
Knowledge GraphsLanguage ModelingLanguage ModellingLarge Language Model+1