Papers Prompt Learning
“Prompt Learning” 태그가 달린 논문 678편 · 필터 해제
Think Small, Act Big: Primitive Prompt Learning for Lifelong Robot Manipulation
Building a lifelong robot that can effectively leverage prior knowledge for continuous skill acquisition remains significantly challenging. Despite the success of experience replay and parameter-efficient methods in alle…
Prompt LearningRobot ManipulationTransfer LearningMPDrive: Improving Spatial Understanding with Marker-Based Prompt Learning for Autonomous Driving
Autonomous driving visual question answering (AD-VQA) aims to answer questions related to perception, prediction, and planning based on given driving scene images, heavily relying on the model's spatial understanding cap…
Autonomous DrivingPrompt LearningQuestion AnsweringVisual Question Answering+1FLIP: Towards Comprehensive and Reliable Evaluation of Federated Prompt Learning
The increasing emphasis on privacy and data security has driven the adoption of federated learning, a decentralized approach to train machine learning models without sharing raw data. Prompt learning, which fine-tunes pr…
Federated LearningPrompt LearningGAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization
Recent advances in large language models have highlighted the critical need for precise control over model outputs through predefined constraints. While existing methods attempt to achieve this through either direct inst…
HallucinationPrompt LearningRethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector
Deepfake detection is a long-established research topic vital for mitigating the spread of malicious misinformation. Unlike prior methods that provide either binary classification results or textual explanations separate…
Binary ClassificationDeepFake DetectionExplanation GenerationFace Swapping+4Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability
Language Bottleneck Models (LBMs) are proposed to achieve interpretable image recognition by classifying images based on textual concept bottlenecks. However, current LBMs simply list all concepts together as the bottlen…
AttributePrompt LearningTowards Efficient and General-Purpose Few-Shot Misclassification Detection for Vision-Language Models
Reliable prediction by classifiers is crucial for their deployment in high security and dynamically changing situations. However, modern neural networks often exhibit overconfidence for misclassified predictions, highlig…
Prompt LearningEnhanced OoD Detection through Cross-Modal Alignment of Multi-Modal Representations
Prior research on out-of-distribution detection (OoDD) has primarily focused on single-modality models. Recently, with the advent of large-scale pretrained vision-language models such as CLIP, OoDD methods utilizing such…
cross-modal alignmentImage ClassificationOut-of-Distribution DetectionPrompt LearningSlide-Level Prompt Learning with Vision Language Models for Few-Shot Multiple Instance Learning in Histopathology
In this paper, we address the challenge of few-shot classification in histopathology whole slide images (WSIs) by utilizing foundational vision-language models (VLMs) and slide-level prompt learning. Given the gigapixel …
Multiple Instance LearningPrompt Learningwhole slide imagesPE-CLIP: A Parameter-Efficient Fine-Tuning of Vision Language Models for Dynamic Facial Expression Recognition
Vision-Language Models (VLMs) like CLIP offer promising solutions for Dynamic Facial Expression Recognition (DFER) but face challenges such as inefficient full fine-tuning, high complexity, and poor alignment between tex…
Dynamic Facial Expression RecognitionFacial Expression Recognitionparameter-efficient fine-tuningPrompt LearningPromptHash: Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval
Cross-modal hashing is a promising approach for efficient data retrieval and storage optimization. However, contemporary methods exhibit significant limitations in semantic preservation, contextual integrity, and informa…
Contrastive LearningCross-Modal RetrievalImage RetrievalImage to text+2OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP
We introduce Low-Shot Open-Set Domain Generalization (LSOSDG), a novel paradigm unifying low-shot learning with open-set domain generalization (ODG). While prompt-based methods using models like CLIP have advanced DG, th…
Domain GeneralizationPrompt LearningProbabilistic Prompt Distribution Learning for Animal Pose Estimation
Multi-species animal pose estimation has emerged as a challenging yet critical task, hindered by substantial visual diversity and uncertainty. This paper challenges the problem by efficient prompt learning for Vision-Lan…
Animal Pose EstimationDiversityPose EstimationPrompt LearningEvolution-based Region Adversarial Prompt Learning for Robustness Enhancement in Vision-Language Models
Large pre-trained vision-language models (VLMs), such as CLIP, demonstrate impressive generalization but remain highly vulnerable to adversarial examples (AEs). Previous work has explored robust text prompts through adve…
Adversarial RobustnessPrompt LearningTLAC: Two-stage LMM Augmented CLIP for Zero-Shot Classification
Contrastive Language-Image Pretraining (CLIP) has shown impressive zero-shot performance on image classification. However, state-of-the-art methods often rely on fine-tuning techniques like prompt learning and adapter-ba…
Domain Generalizationimage-classificationImage ClassificationPrompt Learning+4Modeling Thousands of Human Annotators for Generalizable Text-to-Image Person Re-identification
Text-to-image person re-identification (ReID) aims to retrieve the images of an interested person based on textual descriptions. One main challenge for this task is the high cost in manually annotating large-scale databa…
ClusteringDiversityPerson Re-IdentificationPrompt LearningDP-GPL: Differentially Private Graph Prompt Learning
Graph Neural Networks (GNNs) have shown remarkable performance in various applications. Recently, graph prompt learning has emerged as a powerful GNN training paradigm, inspired by advances in language and vision foundat…
Inference AttackMembership Inference AttackPrompt LearningPrompt to Restore, Restore to Prompt: Cyclic Prompting for Universal Adverse Weather Removal
Universal adverse weather removal (UAWR) seeks to address various weather degradations within a unified framework. Recent methods are inspired by prompt learning using pre-trained vision-language models (e.g., CLIP), lev…
Image RestorationPrompt LearningOpenVidVRD: Open-Vocabulary Video Visual Relation Detection via Prompt-Driven Semantic Space Alignment
The video visual relation detection (VidVRD) task is to identify objects and their relationships in videos, which is challenging due to the dynamic content, high annotation costs, and long-tailed distribution of relation…
Prompt LearningRelationVideo Visual Relation DetectionUrban Region Representation Learning: A Flexible Approach
The increasing availability of urban data offers new opportunities for learning region representations, which can be used as input to machine learning models for downstream tasks such as check-in or crime prediction. Whi…
Crime PredictionPrompt LearningRepresentation Learning