7 citations · 14 across the 16 of their papers we have counts for
16 papers
Neuron: Learning Context-Aware Evolving Representations for Zero-Shot Skeleton Action Recognition
Yang Chen, Jingcai Guo, Song Guo +1
Zero-shot skeleton action recognition is a non-trivial task that requires robust unseen generalization with prior knowledge from only seen classes and shared semantics. Existing me…
Epsilon: Exploring Comprehensive Visual-Semantic Projection for Multi-Label Zero-Shot Learning
Ziming Liu, Jingcai Guo, Song Guo +1
This paper investigates a challenging problem of zero-shot learning in the multi-label scenario (MLZSL), wherein the model is trained to recognize multiple unseen classes within a…
Personalized Federated Domain-Incremental Learning based on Adaptive Knowledge Matching
Yichen Li, Wenchao Xu, Haozhao Wang +3
This paper focuses on Federated Domain-Incremental Learning (FDIL) where each client continues to learn incremental tasks where their domain shifts from each other. We propose a no…
Dual Expert Distillation Network for Generalized Zero-Shot Learning
Zhijie Rao, Jingcai Guo, Xiaocheng Lu +5
Zero-shot learning has consistently yielded remarkable progress via modeling nuanced one-to-one visual-attribute correlation. Existing studies resort to refining a uniform mapping…
Fine-Grained Side Information Guided Dual-Prompts for Zero-Shot Skeleton Action Recognition
Yang Chen, Jingcai Guo, Tian He +1
Skeleton-based zero-shot action recognition aims to recognize unknown human actions based on the learned priors of the known skeleton-based actions and a semantic descriptor space…
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
Sikai Bai, Jie Zhang, Shuaicheng Li +5
Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target doma…