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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.
Human Motion Prediction via Test-domain-aware Adaptation with Easily-available Human Motions Estimated from Videos
TACOS: Temporally-aligned Audio CaptiOnS for Language-Audio Pretraining
Beyond CLIP Generalization: Against Forward&Backward Forgetting Adapter for Continual Learning of Vision-Language Models
DeltaEdit: Enhancing Sequential Editing in Large Language Models by Controlling Superimposed Noise
Image Classification Using a Diffusion Model as a Pre-Training Model
Implementing Long Text Style Transfer with LLMs through Dual-Layered Sentence and Paragraph Structure Extraction and Mapping
Boltzmann Classifier: A Thermodynamic-Inspired Approach to Supervised Learning
Collecting Human Motion Data in Large and Occlusion-Prone Environments using Ultra-Wideband Localization
Zero Dynamics Attack Detection and Isolation in Cyber-Physical Systems with Event-triggered Communication
MM-Skin: Enhancing Dermatology Vision-Language Model with an Image-Text Dataset Derived from Textbooks
The Pitfalls of Growing Group Complexity: LLMs and Social Choice-Based Aggregation for Group Recommendations
FG-CLIP: Fine-Grained Visual and Textual Alignment
scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction
Closing the Loop: Motion Prediction Models beyond Open-Loop Benchmarks
Dynamic Network Flow Optimization for Task Scheduling in PTZ Camera Surveillance Systems
Exploring Zero-Shot App Review Classification with ChatGPT: Challenges and Potential
Interpretable Zero-shot Learning with Infinite Class Concepts
CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection
Fill the Gap: Quantifying and Reducing the Modality Gap in Image-Text Representation Learning
Action Spotting and Precise Event Detection in Sports: Datasets, Methods, and Challenges
Advancing Email Spam Detection: Leveraging Zero-Shot Learning and Large Language Models
Helping Large Language Models Protect Themselves: An Enhanced Filtering and Summarization System
BalancEdit: Dynamically Balancing the Generality-Locality Trade-off in Multi-modal Model Editing
On the effectiveness of Large Language Models in the mechanical design domain
Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction
Investigating Task Arithmetic for Zero-Shot Information Retrieval
SetKE: Knowledge Editing for Knowledge Elements Overlap
DeeCLIP: A Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated Images
Beyond Labels: Zero-Shot Diabetic Foot Ulcer Wound Segmentation with Self-attention Diffusion Models and the Potential for Text-Guided Customization
A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment
Bridge the Gap: From Weak to Full Supervision for Temporal Action Localization with PseudoFormer
Think2SQL: Reinforce LLM Reasoning Capabilities for Text2SQL
Tell Me What You Know About Sexism: Expert-LLM Interaction Strategies and Co-Created Definitions for Zero-Shot Sexism Detection
Functional Abstraction of Knowledge Recall in Large Language Models
REDEditing: Relationship-Driven Precise Backdoor Poisoning on Text-to-Image Diffusion Models
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