7 citations · 18 across the 20 of their papers we have counts for
20 papers
Human-Centric Intelligence in the Era of Foundation Models: A Survey
Yang Chen, Tianqi Wang, Xiaorui Jiang +13
Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling. Yet it has not fully integrated w…
Learning by Neighbor-Aware Semantics, Deciding by Open-form Flows: Towards Robust Zero-Shot Skeleton Action Recognition
Yang Chen, Miaoge Li, Zhijie Rao +3
Recognizing unseen skeleton action categories remains highly challenging due to the absence of corresponding skeletal priors. Existing approaches generally follow an ``align-then-c…
Towards Robust and Realistic Human Pose Estimation via WiFi Signals
Yang Chen, Jingcai Guo
Robust WiFi-based human pose estimation (HPE) is a challenging task that bridges discrete and subtle WiFi signals to human skeletons. We revisit this problem and reveal two critica…
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…
TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learning
Miaoge Li, Jingcai Guo, Richard Yi Da Xu +4
Compositional Zero-Shot Learning (CZSL) aims to recognize novel state-object compositions by leveraging the shared knowledge of their primitive components. Despite considerable pro…
On the Element-Wise Representation and Reasoning in Zero-Shot Image Recognition: A Systematic Survey
Jingcai Guo, Zhijie Rao, Zhi Chen +3
Zero-shot image recognition (ZSIR) aims to recognize and reason in unseen domains by learning generalized knowledge from limited data in the seen domain. The gist of ZSIR is constr…
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…
Attribute-Aware Representation Rectification for Generalized Zero-Shot Learning
Zhijie Rao, Jingcai Guo, Xiaocheng Lu +5
Generalized Zero-shot Learning (GZSL) has yielded remarkable performance by designing a series of unbiased visual-semantics mappings, wherein, the precision relies heavily on the c…
FreePIH: Training-Free Painterly Image Harmonization with Diffusion Model
Ruibin Li, Jingcai Guo, Song Guo +2
This paper provides an efficient training-free painterly image harmonization (PIH) method, dubbed FreePIH, that leverages only a pre-trained diffusion model to achieve state-of-the…
GBE-MLZSL: A Group Bi-Enhancement Framework for Multi-Label Zero-Shot Learning
Ziming Liu, Jingcai Guo, Xiaocheng Lu +3
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…
SRCD: Semantic Reasoning with Compound Domains for Single-Domain Generalized Object Detection
Zhijie Rao, Jingcai Guo, Luyao Tang +3
This paper provides a novel framework for single-domain generalized object detection (i.e., Single-DGOD), where we are interested in learning and maintaining the semantic structure…
SFP: Spurious Feature-targeted Pruning for Out-of-Distribution Generalization
Yingchun Wang, Jingcai Guo, Yi Liu +4
Model substructure learning aims to find an invariant network substructure that can have better out-of-distribution (OOD) generalization than the original full structure. Existing…
Dissecting Arbitrary-scale Super-resolution Capability from Pre-trained Diffusion Generative Models
Ruibin Li, Qihua Zhou, Song Guo +5
Diffusion-based Generative Models (DGMs) have achieved unparalleled performance in synthesizing high-quality visual content, opening up the opportunity to improve image super-resol…
DRPT: Disentangled and Recurrent Prompt Tuning for Compositional Zero-Shot Learning
Xiaocheng Lu, Ziming Liu, Song Guo +4
Compositional Zero-shot Learning (CZSL) aims to recognize novel concepts composed of known knowledge without training samples. Standard CZSL either identifies visual primitives or…
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning
Yingchun Wang, Jingcai Guo, Song Guo +1
Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining sub-optimal…
Towards Fairer and More Efficient Federated Learning via Multidimensional Personalized Edge Models
Yingchun Wang, Jingcai Guo, Jie Zhang +3
Federated learning (FL) is an emerging technique that trains massive and geographically distributed edge data while maintaining privacy. However, FL has inherent challenges in term…
Exploring Optimal Substructure for Out-of-distribution Generalization via Feature-targeted Model Pruning
Yingchun Wang, Jingcai Guo, Song Guo +2
Recent studies show that even highly biased dense networks contain an unbiased substructure that can achieve better out-of-distribution (OOD) generalization than the original model…
Decomposed Soft Prompt Guided Fusion Enhancing for Compositional Zero-Shot Learning
Xiaocheng Lu, Ziming Liu, Song Guo +1
Compositional Zero-Shot Learning (CZSL) aims to recognize novel concepts formed by known states and objects during training. Existing methods either learn the combined state-object…
ProCC: Progressive Cross-primitive Compatibility for Open-World Compositional Zero-Shot Learning
Fushuo Huo, Wenchao Xu, Song Guo +4
Open-World Compositional Zero-shot Learning (OW-CZSL) aims to recognize novel compositions of state and object primitives in images with no priors on the compositional space, which…