Papers with Code 아카이브

Zero-Shot Learning

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

하위 태스크: 7
평가표: 33
보존된 평가 기록: 84

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 )

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Zero-Shot Learning

논문

Enabling Low-Resource Language Retrieval: Establishing Baselines for Urdu MS MARCO

Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing

ConceptEdit: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning

Multi-Scale Incremental Modeling for Enhanced Human Motion Prediction in Human-Robot Collaboration

Discrepancy-Aware Attention Network for Enhanced Audio-Visual Zero-Shot Learning

Advancing Comprehensive Aesthetic Insight with Multi-Scale Text-Guided Self-Supervised Learning

An Efficient Framework for Enhancing Discriminative Models via Diffusion Techniques

Temporal Action Localization with Cross Layer Task Decoupling and Refinement

USDRL: Unified Skeleton-Based Dense Representation Learning with Multi-Grained Feature Decorrelation

Can Graph Neural Networks Learn Language with Extremely Weak Text Supervision?

Model-Editing-Based Jailbreak against Safety-aligned Large Language Models

Zero-Shot Mono-to-Binaural Speech Synthesis

Assessing Personalized AI Mentoring with Large Language Models in the Computing Field

SenCLIP: Enhancing zero-shot land-use mapping for Sentinel-2 with ground-level prompting

GPD-1: Generative Pre-training for Driving

Retaining and Enhancing Pre-trained Knowledge in Vision-Language Models with Prompt Ensembling

Compositional Zero-Shot Learning with Contextualized Cues and Adaptive Contrastive Training

Privacy-Preserving Customer Support: A Framework for Secure and Scalable Interactions

Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control

Explaining and Mitigating the Modality Gap in Contrastive Multimodal Learning

Robust Multiple Description Neural Video Codec with Masked Transformer for Dynamic and Noisy Networks

DenseVLM: A Retrieval and Decoupled Alignment Framework for Open-Vocabulary Dense Prediction

JAPAGEN: Efficient Few/Zero-shot Learning via Japanese Training Dataset Generation with LLM

KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment

$S^3$: Synonymous Semantic Space for Improving Zero-Shot Generalization of Vision-Language Models

PriorMotion: Generative Class-Agnostic Motion Prediction with Raster-Vector Motion Field Priors

Automated Medical Report Generation for ECG Data: Bridging Medical Text and Signal Processing with Deep Learning

Unified Framework for Open-World Compositional Zero-shot Learning

D-LORD for Motion Stylization

Expanding Event Modality Applications through a Robust CLIP-Based Encoder

Gaussian Processes for Probabilistic Estimates of Earthquake Ground Shaking: A 1-D Proof-of-Concept

Diffusion in Zero-Shot Learning for Environmental Audio

Multimodal Remote Sensing Scene Classification Using VLMs and Dual-Cross Attention Networks

Unveiling Concept Attribution in Diffusion Models

Optimizing Large Language Models for Turkish: New Methodologies in Corpus Selection and Training

Enhancing Robustness of CLIP to Common Corruptions through Bimodal Test-Time Adaptation