Papers Zero-Shot Learning
“Zero-Shot Learning” 태그가 달린 논문 1,940편 · 필터 해제
Attribute Distribution Modeling and Semantic-Visual Alignment for Generative Zero-shot Learning
Generative zero-shot learning (ZSL) synthesizes features for unseen classes, leveraging semantic conditions to transfer knowledge from seen classes. However, it also introduces two intrinsic challenges: (1) class-level a…
Zero-Shot LearningCLIP-driven Zero-shot Learning with Ambiguous Labels
Zero-shot learning (ZSL) aims to recognize unseen classes by leveraging semantic information from seen classes, but most existing methods assume accurate class labels for training instances. However, in real-world scenar…
Zero-Shot LearningExtending $μ$P: Spectral Conditions for Feature Learning Across Optimizers
Several variations of adaptive first-order and second-order optimization methods have been proposed to accelerate and scale the training of large language models. The performance of these optimization routines is highly …
Zero-Shot LearningHandling Supervision Scarcity in Chest X-ray Classification: Long-Tailed and Zero-Shot Learning
Chest X-Ray (CXR) classification in clinical practice is often limited by imperfect supervision, arising from (i) extreme long-tailed multi-label disease distributions and (ii) missing annotations for rare or previously …
Multi-Label ClassificationMulti-Label LearningZero-Shot LearningZeroDiff++: Substantial Unseen Visual-semantic Correlation in Zero-shot Learning
Zero-shot Learning (ZSL) enables classifiers to recognize classes unseen during training, commonly via generative two stage methods: (1) learn visual semantic correlations from seen classes; (2) synthesize unseen class f…
Zero-Shot LearningMOSLD-Bench: Multilingual Open-Set Learning and Discovery Benchmark for Text Categorization
Open-set learning and discovery (OSLD) is a challenging machine learning task in which samples from new (unknown) classes can appear at test time. It can be seen as a generalization of zero-shot learning, where the new c…
Text ClassificationZero-Shot LearningA Single Architecture for Representing Invariance Under Any Space Group
Incorporating known symmetries in data into machine learning models has consistently improved predictive accuracy, robustness, and generalization. However, achieving exact invariance to specific symmetries typically requ…
Zero-Shot LearningFloorplan2Guide: LLM-Guided Floorplan Parsing for BLV Indoor Navigation
Indoor navigation remains a critical challenge for people with visual impairments. The current solutions mainly rely on infrastructure-based systems, which limit their ability to navigate safely in dynamic environments. …
Zero-Shot LearningFew-Shot LearningKnowledge GraphsVisual ReasoningFine-Grained Zero-Shot Learning with Attribute-Centric Representations
Recognizing unseen fine-grained categories demands a model that can distinguish subtle visual differences. This is typically achieved by transferring visual-attribute relationships from seen classes to unseen classes. Th…
Representation LearningZero-Shot LearningVision-Language Models for Infrared Industrial Sensing in Additive Manufacturing Scene Description
Many manufacturing environments operate in low-light conditions or within enclosed machines where conventional vision systems struggle. Infrared cameras provide complementary advantages in such environments. Simultaneous…
Zero-Shot LearningHybrid Synthetic Data Generation with Domain Randomization Enables Zero-Shot Vision-Based Part Inspection Under Extreme Class Imbalance
Machine learning, particularly deep learning, is transforming industrial quality inspection. Yet, training robust machine learning models typically requires large volumes of high-quality labeled data, which are expensive…
Synthetic Data GenerationZero-Shot LearningObject DetectionRethinking Plant Disease Diagnosis: Bridging the Academic-Practical Gap with Vision Transformers and Zero-Shot Learning
Recent advances in deep learning have enabled significant progress in plant disease classification using leaf images. Much of the existing research in this field has relied on the PlantVillage dataset, which consists of …
Zero-Shot LearningDomain AdaptationZero-Training Task-Specific Model Synthesis for Few-Shot Medical Image Classification
Deep learning models have achieved remarkable success in medical image analysis but are fundamentally constrained by the requirement for large-scale, meticulously annotated datasets. This dependency on "big data" is a cr…
Medical Image ClassificationZero-Shot LearningLAUD: Integrating Large Language Models with Active Learning for Unlabeled Data
Large language models (LLMs) have shown a remarkable ability to generalize beyond their pre-training data, and fine-tuning LLMs can elevate performance to human-level and beyond. However, in real-world scenarios, lacking…
Zero-Shot LearningFew-Shot LearningActive LearningCoS: Towards Optimal Event Scheduling via Chain-of-Scheduling
Recommending event schedules is a key issue in Event-based Social Networks (EBSNs) in order to maintain user activity. An effective recommendation is required to maximize the user's preference, subjecting to both time an…
Knowledge DistillationZero-Shot LearningMulti-Granularity Mutual Refinement Network for Zero-Shot Learning
Zero-shot learning (ZSL) aims to recognize unseen classes with zero samples by transferring semantic knowledge from seen classes. Current approaches typically correlate global visual features with semantic information (i…
Zero-Shot LearningDistributed Zero-Shot Learning for Visual Recognition
In this paper, we propose a Distributed Zero-Shot Learning (DistZSL) framework that can fully exploit decentralized data to learn an effective model for unseen classes. Considering the data heterogeneity issues across di…
Zero-Shot LearningDual-branch Spatial-Temporal Self-supervised Representation for Enhanced Road Network Learning
Road network representation learning (RNRL) has attracted increasing attention from both researchers and practitioners as various spatiotemporal tasks are emerging. Recent advanced methods leverage Graph Neural Networks …
Representation LearningContrastive LearningZero-Shot LearningAchieving Effective Virtual Reality Interactions via Acoustic Gesture Recognition based on Large Language Models
Natural and efficient interaction remains a critical challenge for virtual reality and augmented reality (VR/AR) systems. Vision-based gesture recognition suffers from high computational cost, sensitivity to lighting con…
Gesture RecognitionZero-Shot LearningDecoupling Augmentation Bias in Prompt Learning for Vision-Language Models
Recent advances in large-scale vision and language models have led to significant progress in zero-shot learning tasks. Methods such as CoOp and CoCoOp have shown that replacing handcrafted prompts with learnable vectors…
Domain GeneralizationZero-Shot LearningData Augmentation