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Domain Adaptation

  • [NIPS 2017] Few-Shot Adversarial Domain Adaptation
  • [ICCV 2019] Bidirectional One-Shot Unsupervised Domain Mapping
  • [ICLR 2020 spotlight] (paper ) Cross-domain Few-shot Classification via Learned Feature-wise Transformation
    • Plug in addon parameters to adjust between unseen and seen encoders.
    • Training in pair-wise sample strategy between seen domain and unseen domain.
  • [ICLR 2020] Meta-learning curiosity algorithms
  • [ICLR 2020] Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
  • [ICML 2020] (paper) Few-shot Domain Adaptation by Causal Mechanism Transfer
  • [NIPS 2020] CrossTransformers: spatially-aware few-shot transfer
    • Using transformer as encoder and query images performed as query keys
  • [ICLR 2021] (paper) A UNIVERSAL REPRESENTATION TRANSFORMER LAYER FOR FEW-SHOT IMAGE CLASSIFICATION
    • multi-domain few-shot image classification
  • [ICLR 2021] (paper) REPURPOSING PRETRAINED MODELS FOR ROBUST OUT-OF-DOMAIN FEW-SHOT LEARNING
  • [ICLR 2021] (paper) SSD: A Unified Framework for Self-Supervised Outlier Detection

Reinforcement Learning

  • [ICML 2019] Few-Shot Intent Inference via Meta-Inverse Reinforcement Learning
  • [ICLR 2020] Meta Reinforcement Learning with Autonomous Inference of Subtask Dependencies
  • [ICLR 2020] VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
  • [NIPS 2020] (paper) One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL

Visual Tracking

  • [ICCV 2019] Deep Meta Learning for Real-Time Target-Aware Visual Tracking
  • [CVPR 2020] (paper) Tracking by Instance Detection: A Meta-Learning Approach
    • MAML-Tracker

Theoritical

  • [ICLR 2020 Bengio] A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
    • Argue that how to fast adapt to new distributions by meta-learn causal structures
    • Also have follow paper on arxiv here

Recommendation System

  • [SKIM 2020] Learning to Profile: User Meta-Profile Network for Few-Shot Learning
    • Interesting Time sequence encoding methods
    • Using meta-learning methods to learn user-profile representations
    • Can be used to solve data scarcity or class imbalance problem

Graph Classification

  • [CIKM 2020] Adaptive-Step Graph Meta-Learner for Few-Shot Graph Classification
  • [CIKM 2020] Graph Prototypical Networks for Few-shot Learning on Attributed Networks
    • Graph node classification
  • [CIKM 2020] Graph Few-shot Learning with Attribute Matching
  • [NIPS 2020] (paper) Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding
  • [AAAI 2021] (paper) Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph

Others

  • [IJCAI 2019] Incremental Few-Shot Learning for Pedestrian Attribute Recognition

  • [AAAI 2018] AffinityNet- Semi-supervised Few-shot Learning for Disease Type Prediction Use few-shot method to enhance urinal disease type prediction

  • [NIPS 2018] Neural Voice Cloning with a Few Samples

  • [ICCV 2019] (paper) ACMM: Aligned Cross-Modal Memory for Few-Shot Image and Sentence Matching

    • Image and Sentence Matching

  • [ICCV 2019] (RECOMMANDED!) Task-Driven Modular Networks for Zero-Shot Compositional Learning

    • An interesting usage of a bunch of MLPs.

  • [CVPR 2019] (paper code) SAR Image Classification Using Few-shot Cross-domain Transfer Learning

  • [ICLR 2020] FEW-SHOT TEXT CLASSIFICATION WITH DISTRIBUTIONAL SIGNATURES

  • [ICLR 2020] METAPIX: FEW-SHOT VIDEO RETARGETING

  • [ICLR 2020] ONE-SHOT PRUNING OF RECURRENT NEURAL NETWORKS BY JACOBIAN SPECTRUM EVALUATION

    • Pruning

  • [ICLR 2020] TOWARDS FAST ADAPTATION OF NEURAL ARCHITECTURES WITH META LEARNING

    • NAS

  • [ICLR 2020] META-DATASET: A DATASET OF DATASETS FOR LEARNING TO LEARN FROM FEW EXAMPLES

    • New datasets

  • [ECCV 2018] Few-Shot Human Motion Prediction via Meta-Learning

  • [ECCV 2018] Compound Memory Networks for Few-shot Video Classification

  • [CVPR 2019] Coloring With Limited Data: Few-Shot Colorization via Memory Augmented Networks

  • [CVPR 2019] Improving Few-Shot User-Specific Gaze Adaptation via Gaze Redirection Synthesis

  • [CVPR 2019] Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning

  • [ICCV 2019] Few-Shot Adaptive Gaze Estimation

  • [ICCV 2019] One-Shot Neural Architecture Search via Self-Evaluated Template Network

  • [AAAI 2020] Learning Meta Model for Zero- and Few-shot Face Anti-spoofing

  • [AAAI 2020] Graph Few-shot Learning via Knowledge Transfer

  • [AAAI 2020] Few Shot Network Compression via Cross Distillation (模型压缩)

  • [AAAI 2020] Few-Shot Bayesian Imitation Learning with Logical Program Policies

  • [CVPR 2020] Meta-Transfer Learning for Zero-Shot Super-Resolution

  • [CVPR 2020] Learning from Web Data with Self-Organizing Memory Module

    • solve label noise and background noise in the images with memory module [CVPR 2020] Single-view view synthesis with multi plane images

  • [AAAI 2020] (paper) SGAP-Net: Semantic-Guided Attentive Prototypes Network for Few-Shot Human-Object Interaction Recognition

  • [ICLR 2020] MetaPix: Few-Shot Video Retargeting

  • [ICLR 2020] (paper) Towards Fast Adaptation of Neural Architectures with Meta Learning

  • [ICLR 2020] Query-efficient Meta Attack to Deep Neural Networks

  • [SIGGRAPH Asia 2019] Artistic Glyph Image Synthesis via One-Stage Few-Shot Learning

  • [IJCNN 2020] Interpretable Time-series Classification on Few-shot Samples

  • [CVPR 2018] (paper) Temporal Hallucinating for Action Recognition with Few Still Images

    • Attempt to recall cues from relevant action videos.
    • Maybe good at one-shot, not worse than the baseline in 5-shot and 10-shot scenarios.

  • [ECCV 2020] n-Reference Transfer Learning for Saliency Prediction

  • [ICML 2020] Meta-learning with Stochastic Linear Bandits

    • Linear Bandits itself is a task

  • [ECCV 2020 spotlight] Few-shot Action Recognition via Permutation-invariant Attention

  • [ECCV 2020 spotlight] Few-Shot Scene-Adaptive Anomaly Detection

  • [NIPS 2020] Self-Supervised Few-Shot Learning on Point Clouds

  • [MICAI 2020] (paper) Domain Generalizer: A Few-shot Meta Learning Framework for Domain Generalization in Medical Imaging

  • [ICML 2020] (paper) Informative Dropout for Robust Representation Learning: A Shape-bias Perspective

  • [IEEE IROS 2020] (paper) Tell me what this is: Few-Shot Incremental Object Learning by a Robot

  • [CIKM 2020] Few-shot Insider Threat Detection

  • [ACMMM 2020] Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition

  • [NIPS 2020] Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning

    • Visual Reasoning

  • [NIPS 2020] OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification

  • [NIPS 2020] Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

    • Adversarially Robust FSL

  • [CVPR 2020] Multi-Domain Learning for Accurate and Few-Shot Color Constancy

  • [CVPR 2020] Few-Shot Video Classification via Temporal Alignment

  • [CVPR 2020] Few-Shot Pill Recognition

  • [CVPR 2020] Learning to Select Base Classes for Few-shot Classification

  • [CVPR 2020] Few-Shot Open-Set Recognition using Meta-Learning

  • [NIPS 2020] (paper) Event Guided Denoising for Multilingual Relation Learning

  • [AAAI 2021] Progressive Network Grafting for Few-Shot Knowledge Distillation

  • [NIPS 2020] ADVERSARIALLY ROBUST FEW-SHOT LEARNING: A META-LEARNING APPROACH

    • A approach is robust to adversarial attack

  • [ICLR 2021] (paper) FEW-SHOT BAYESIAN OPTIMIZATION WITH DEEP KERNEL SURROGATES

    • Hyper Parameter Optimization

  • [TPMAI 2021] (paper) Domain-Specific Priors and Meta Learning for Few-Shot First-Person Action Recognition

  • [WWW 2021] (paper) Few-Shot Graph Learning for Molecular Property Prediction

  • [AAAI 2021] (paper) Progressive Network Grafting for Few-Shot Knowledge Distillation

  • [CIKM 2020] (paper) Graph Few-shot Learning with Attribute Matching

    • Few-shot learning in structed datas

  • [AAAI 2021] (paper) Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks

  • [SIGIR 2021] Not All Relevance Scores are Equal: Efficient Uncertainty and Calibration Modeling for Deep Retrieval Models

  • [IJCAI 2021] Few-shot Neural Human Performance Rendering from Sparse RGBD Videos

  • [IJCAI 2021] Learning Implicit Temporal Alignment for Few-shot Video Classification

  • [CVPR 2021] Few-Shot Human Motion Transfer by Personalized Geometry and Texture Modeling

  • [ICML 2021] Few-Shot Neural Architecture Search

  • [ICCV 2021] Video Pose Distillation for Few-Shot, Fine-Grained Sports Action Recognition

  • [ACMMM 2021] (paper) Few-Shot Fine-Grained Action Recognition via Bidirectional Attention and Contrastive Meta-Learning

  • [CVPR 2021] (paper) Dynamic Class Queue for Large Scale Face Recognition In the Wild