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Zero-Shot Learning
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Zero-shot learning (ZSL) is a model's ability to detect classes never seen during training. The condition is that the classes are not known during supervised learning.
Earlier work in zero-shot learning use attributes in a two-step approach to infer unknown classes. In the computer vision context, more recent advances learn mappings from image feature space to semantic space. Other approaches learn non-linear multimodal embeddings. In the modern NLP context, language models can be evaluated on downstream tasks without fine tuning.
Benchmark datasets for zero-shot learning include aPY, AwA, and CUB, among others.
( Image credit: Prototypical Networks for Few shot Learning in PyTorch )
Further readings:
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning
Large Language Models are Unreliable for Cyber Threat Intelligence
Action Recognition in Real-World Ambient Assisted Living Environment
Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models
Extremely Simple Out-of-distribution Detection for Audio-visual Generalized Zero-shot Learning
DF-Net: The Digital Forensics Network for Image Forgery Detection
Gaze-Guided 3D Hand Motion Prediction for Detecting Intent in Egocentric Grasping Tasks
Context-Aware Weakly Supervised Image Manipulation Localization with SAM Refinement
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
ADS-Edit: A Multimodal Knowledge Editing Dataset for Autonomous Driving Systems
Enhancing Small Language Models for Cross-Lingual Generalized Zero-Shot Classification with Soft Prompt Tuning
fine-CLIP: Enhancing Zero-Shot Fine-Grained Surgical Action Recognition with Vision-Language Models
Self-Explaining Neural Networks for Business Process Monitoring
Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection
Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition
CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners
CAARMA: Class Augmentation with Adversarial Mixup Regularization
Semantic Segmentation of Transparent and Opaque Drinking Glasses with the Help of Zero-shot Learning
LEGION: Learning to Ground and Explain for Synthetic Image Detection
Towards Achieving Perfect Multimodal Alignment
Sparseformer: a Transferable Transformer with Multi-granularity Token Sparsification for Medical Time Series Classification
Leveraging MoE-based Large Language Model for Zero-Shot Multi-Task Semantic Communication
Persistent Homology-induced Graph Ensembles for Time Series Regressions
Advancing Medical Representation Learning Through High-Quality Data
Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection
Real-Time Cell Sorting with Scalable In Situ FPGA-Accelerated Deep Learning
TLAC: Two-stage LMM Augmented CLIP for Zero-Shot Classification
Zero-TIG: Temporal Consistency-Aware Zero-Shot Illumination-Guided Low-light Video Enhancement
An experimental approach on Few Shot Class Incremental Learning
Systematic Classification of Studies Investigating Social Media Conversations about Long COVID Using a Novel Zero-Shot Transformer Framework
MoFlow: One-Step Flow Matching for Human Trajectory Forecasting via Implicit Maximum Likelihood Estimation based Distillation
SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning
Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images
Trajectory Mamba: Efficient Attention-Mamba Forecasting Model Based on Selective SSM
ChatGPT Encounters Morphing Attack Detection: Zero-Shot MAD with Multi-Modal Large Language Models and General Vision Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.