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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.
The use of large language models to enhance cancer clinical trial educational materials
Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP
Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey
TAROT: Targeted Data Selection via Optimal Transport
Proto Successor Measure: Representing the Space of All Possible Solutions of Reinforcement Learning
ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
SkelMamba: A State Space Model for Efficient Skeleton Action Recognition of Neurological Disorders
Multimodal Whole Slide Foundation Model for Pathology
Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition
CLIP meets DINO for Tuning Zero-Shot Classifier using Unlabeled Image Collections
An End-to-End Two-Stream Network Based on RGB Flow and Representation Flow for Human Action Recognition
Active Data Curation Effectively Distills Large-Scale Multimodal Models
One Mind, Many Tongues: A Deep Dive into Language-Agnostic Knowledge Neurons in Large Language Models
FLEX-CLIP: Feature-Level GEneration Network Enhanced CLIP for X-shot Cross-modal Retrieval
DECODE: Domain-aware Continual Domain Expansion for Motion Prediction
Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals
MotionWavelet: Human Motion Prediction via Wavelet Manifold Learning
OccludeNet: A Causal Journey into Mixed-View Actor-Centric Video Action Recognition under Occlusions
TableTime: Reformulating Time Series Classification as Zero-Shot Table Understanding via Large Language Models
ChatBCI: A P300 Speller BCI Leveraging Large Language Models for Improved Sentence Composition in Realistic Scenarios
Lifelong Knowledge Editing for Vision Language Models with Low-Rank Mixture-of-Experts
Adaptive Hyper-Graph Convolution Network for Skeleton-based Human Action Recognition with Virtual Connections
When Spatial meets Temporal in Action Recognition
CLIPer: Hierarchically Improving Spatial Representation of CLIP for Open-Vocabulary Semantic Segmentation
Safety Without Semantic Disruptions: Editing-free Safe Image Generation via Context-preserving Dual Latent Reconstruction
DriveMLLM: A Benchmark for Spatial Understanding with Multimodal Large Language Models in Autonomous Driving
Topological Symmetry Enhanced Graph Convolution for Skeleton-Based Action Recognition
Visual-Oriented Fine-Grained Knowledge Editing for MultiModal Large Language Models
Efficient Transfer Learning for Video-language Foundation Models
Visual-Semantic Graph Matching Net for Zero-Shot Learning
Leveraging MLLM Embeddings and Attribute Smoothing for Compositional Zero-Shot Learning
ModeSeq: Taming Sparse Multimodal Motion Prediction with Sequential Mode Modeling
TDSM: Triplet Diffusion for Skeleton-Text Matching in Zero-Shot Action Recognition
CorrCLIP: Reconstructing Correlations in CLIP with Off-the-Shelf Foundation Models for Open-Vocabulary Semantic Segmentation
DiVR: incorporating context from diverse VR scenes for human trajectory prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.