2025 Poster "few-shot learning" Papers
28 papers found
A Flag Decomposition for Hierarchical Datasets
Nathan Mankovich, Ignacio Santamaria, Gustau Camps-Valls et al.
Are Transformers Able to Reason by Connecting Separated Knowledge in Training Data?
Yutong Yin, Zhaoran Wang
Can LLMs Understand Time Series Anomalies?
Zihao Zhou, Rose Yu
CapeX: Category-Agnostic Pose Estimation from Textual Point Explanation
Matan Rusanovsky, Or Hirschorn, Shai Avidan
Causal Disentanglement and Cross-Modal Alignment for Enhanced Few-Shot Learning
Tianjiao Jiang, Zhen Zhang, Yuhang Liu et al.
DETree: DEtecting Human-AI Collaborative Texts via Tree-Structured Hierarchical Representation Learning
Yongxin He, Shan Zhang, Yixuan Cao et al.
Doctor Approved: Generating Medically Accurate Skin Disease Images through AI-Expert Feedback
Janet Wang, Yunbei Zhang, Zhengming Ding et al.
Federated Few-Shot Class-Incremental Learning
Muhammad Anwar Masum, Mahardhika Pratama, Lin Liu et al.
Few-Shot Image Quality Assessment via Adaptation of Vision-Language Models
Xudong Li, Zihao Huang, Yan Zhang et al.
FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
Kwan Yun, Chaelin Kim, Hangyeul Shin et al.
Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
Zhaochong An, Guolei Sun, Yun Liu et al.
Logits DeConfusion with CLIP for Few-Shot Learning
Shuo Li, Fang Liu, Zehua Hao et al.
Meta-Dynamical State Space Models for Integrative Neural Data Analysis
Ayesha Vermani, Josue Nassar, Hyungju Jeon et al.
MoEMeta: Mixture-of-Experts Meta Learning for Few-Shot Relational Learning
Han Wu, Jie Yin
Multimodality Helps Few-shot 3D Point Cloud Semantic Segmentation
Zhaochong An, Guolei Sun, Yun Liu et al.
PAL: Sample-Efficient Personalized Reward Modeling for Pluralistic Alignment
Daiwei Chen, Yi Chen, Aniket Rege et al.
Personalized Representation from Personalized Generation
Shobhita Sundaram, Julia Chae, Yonglong Tian et al.
Preference-driven Knowledge Distillation for Few-shot Node Classification
Xing Wei, Chunchun Chen, Rui Fan et al.
Self-Ensembling Gaussian Splatting for Few-Shot Novel View Synthesis
Chen Zhao, Xuan Wang, Tong Zhang et al.
Self-Supervised Contrastive Learning is Approximately Supervised Contrastive Learning
Achleshwar Luthra, Tianbao Yang, Tomer Galanti
Support Vector Generation: Kernelizing Large Language Models for Efficient Zero‑Shot NLP
Shohei Ohsawa
The Devil is in Low-Level Features for Cross-Domain Few-Shot Segmentation
Yuhan Liu, Yixiong Zou, Yuhua Li et al.
Tripartite Weight-Space Ensemble for Few-Shot Class-Incremental Learning
Juntae Lee, Munawar Hayat, Sungrack Yun
UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection
Zhaopeng Gu, Bingke Zhu, Guibo Zhu et al.
Unsupervised Meta-Learning via In-Context Learning
Anna Vettoruzzo, Lorenzo Braccaioli, Joaquin Vanschoren et al.
Unveiling the Learning Mind of Language Models: A Cognitive Framework and Empirical Study
Zhengyu Hu, Jianxun Lian, Zheyuan Xiao et al.
Weak-shot Keypoint Estimation via Keyness and Correspondence Transfer
Junjie Chen, Zeyu Luo, Zezheng Liu et al.
Why In-Context Learning Models are Good Few-Shot Learners?
Shiguang Wu, Yaqing Wang, Quanming Yao