Papers Prompt Learning
“Prompt Learning” 태그가 달린 논문 678편 · 필터 해제
Multiple Information Prompt Learning for Cloth-Changing Person Re-Identification
Cloth-changing person re-identification is a subject closer to the real world, which focuses on solving the problem of person re-identification after pedestrians change clothes. The primary challenge in this field is to …
Cloth-Changing Person Re-IdentificationPerson Re-IdentificationPrompt LearningEZ-HOI: VLM Adaptation via Guided Prompt Learning for Zero-Shot HOI Detection
Detecting Human-Object Interactions (HOI) in zero-shot settings, where models must handle unseen classes, poses significant challenges. Existing methods that rely on aligning visual encoders with large Vision-Language Mo…
Human-Object Interaction DetectionLarge Language ModelPrompt LearningMoTaDual: Modality-Task Dual Alignment for Enhanced Zero-shot Composed Image Retrieval
Composed Image Retrieval (CIR) is a challenging vision-language task, utilizing bi-modal (image+text) queries to retrieve target images. Despite the impressive performance of supervised CIR, the dependence on costly, man…
Image RetrievalPrompt LearningRetrievalTriplet+1Aggregate-and-Adapt Natural Language Prompts for Downstream Generalization of CLIP
Large pretrained vision-language models like CLIP have shown promising generalization capability, but may struggle in specialized domains (e.g., satellite imagery) or fine-grained classification (e.g., car models) where …
Image CaptioningPrompt LearningVisual Question Answering (VQA)IP-MOT: Instance Prompt Learning for Cross-Domain Multi-Object Tracking
Multi-Object Tracking (MOT) aims to associate multiple objects across video frames and is a challenging vision task due to inherent complexities in the tracking environment. Most existing approaches train and track withi…
Knowledge DistillationLanguage ModellingMulti-Object TrackingObject Tracking+2AdaptGCD: Multi-Expert Adapter Tuning for Generalized Category Discovery
Different from the traditional semi-supervised learning paradigm that is constrained by the close-world assumption, Generalized Category Discovery (GCD) presumes that the unlabeled dataset contains new categories not app…
General KnowledgePrompt LearningKnowledge-Guided Prompt Learning for Request Quality Assurance in Public Code Review
Public Code Review (PCR) is developed in the Software Question Answering (SQA) community, assisting developers in exploring high-quality and efficient review services. Current methods on PCR mainly focus on the reviewer'…
Prompt LearningQuestion AnsweringTAGMulti-Class Textual-Inversion Secretly Yields a Semantic-Agnostic Classifier
With the advent of large pre-trained vision-language models such as CLIP, prompt learning methods aim to enhance the transferability of the CLIP model. They learn the prompt given few samples from the downstream task giv…
Prompt LearningReliable and Compact Graph Fine-tuning via GraphSparse Prompting
Recently, graph prompt learning has garnered increasing attention in adapting pre-trained GNN models for downstream graph learning tasks. However, existing works generally conduct prompting over all graph elements (e.g.,…
AttributeGraph LearningPrompt LearningPoint-PRC: A Prompt Learning Based Regulation Framework for Generalizable Point Cloud Analysis
This paper investigates the 3D domain generalization (3DDG) ability of large 3D models based on prevalent prompt learning. Recent works demonstrate the performances of 3D point cloud recognition can be boosted remarkably…
Domain GeneralizationGeneral KnowledgePrompt LearningPrompt Diffusion Robustifies Any-Modality Prompt Learning
Foundation models enable prompt-based classifiers for zero-shot and few-shot learning. Nonetheless, the conventional method of employing fixed prompts suffers from distributional shifts that negatively impact generalizab…
Computational EfficiencyDomain GeneralizationFew-Shot LearningPrompt LearningFairQueue: Rethinking Prompt Learning for Fair Text-to-Image Generation
Recently, prompt learning has emerged as the state-of-the-art (SOTA) for fair text-to-image (T2I) generation. Specifically, this approach leverages readily available reference images to learn inclusive prompts for each t…
AttributeDenoisingFairnessImage Generation+3DreamClear: High-Capacity Real-World Image Restoration with Privacy-Safe Dataset Curation
Image restoration (IR) in real-world scenarios presents significant challenges due to the lack of high-capacity models and comprehensive datasets. To tackle these issues, we present a dual strategy: GenIR, an innovative …
Image RestorationPrompt LearningAMPLE: Emotion-Aware Multimodal Fusion Prompt Learning for Fake News Detection
Detecting fake news in large datasets is challenging due to its diversity and complexity, with traditional approaches often focusing on textual features while underutilizing semantic and emotional elements. Current metho…
Fake News DetectionPrompt LearningSentiment AnalysisAEPL: Automated and Editable Prompt Learning for Brain Tumor Segmentation
Brain tumor segmentation is crucial for accurate diagnosisand treatment planning, but the small size and irregular shapeof tumors pose significant challenges. Existing methods of-ten fail to effectively incorporate medic…
Brain Tumor SegmentationDecoderMulti-Task LearningPrompt Learning+2IPO: Interpretable Prompt Optimization for Vision-Language Models
Pre-trained vision-language models like CLIP have remarkably adapted to various downstream tasks. Nonetheless, their performance heavily depends on the specificity of the input text prompts, which requires skillful promp…
Prompt LearningSpecificityA Persuasion-Based Prompt Learning Approach to Improve Smishing Detection through Data Augmentation
Smishing, which aims to illicitly obtain personal information from unsuspecting victims, holds significance due to its negative impacts on our society. In prior studies, as a tool to counteract smishing, machine learning…
Data AugmentationPrompt LearningAre You Using Reliable Graph Prompts? Trojan Prompt Attacks on Graph Neural Networks
Graph Prompt Learning (GPL) has been introduced as a promising approach that uses prompts to adapt pre-trained GNN models to specific downstream tasks without requiring fine-tuning of the entire model. Despite the advant…
Backdoor AttackPrompt LearningOn the Utility of Domain Modeling Assistance with Large Language Models
Model-driven engineering (MDE) simplifies software development through abstraction, yet challenges such as time constraints, incomplete domain understanding, and adherence to syntactic constraints hinder the design proce…
Prompt LearningAdaptive Prompt Learning with SAM for Few-shot Scanning Probe Microscope Image Segmentation
The Segment Anything Model (SAM) has demonstrated strong performance in image segmentation of natural scene images. However, its effectiveness diminishes markedly when applied to specific scientific domains, such as Scan…
Image SegmentationPrompt LearningSegmentationSemantic Segmentation