20 citations · 47 across the 11 of their papers we have counts for
11 papers
Crafting Your Evolving Dreams: Concept-Incremental Versatile Customization
Jiahua Dong, Wenqi Liang, Hongliu Li +7
Custom diffusion models (CDMs) have garnered significant interest owing to their remarkable capacity for generating personalized concepts. However, the majority of CDMs unrealistic…
Adma-GAN: Attribute-Driven Memory Augmented GANs for Text-to-Image Generation
Xintian Wu, Hanbin Zhao, Liangli Zheng +2
As a challenging task, text-to-image generation aims to generate photo-realistic and semantically consistent images according to the given text descriptions. Existing methods mainl…
RBC: Rectifying the Biased Context in Continual Semantic Segmentation
Hanbin Zhao, Fengyu Yang, Xinghe Fu +1
Recent years have witnessed a great development of Convolutional Neural Networks in semantic segmentation, where all classes of training images are simultaneously available. In pra…
Progressive Class-based Expansion Learning For Image Classification
Hui Wang, Hanbin Zhao, Xi Li
In this paper, we propose a novel image process scheme called class-based expansion learning for image classification, which aims at improving the supervision-stimulation frequency…
When Video Classification Meets Incremental Classes
Hanbin Zhao, Xin Qin, Shihao Su +3
With the rapid development of social media, tremendous videos with new classes are generated daily, which raise an urgent demand for video classification methods that can continuou…
PcmNet: Position-Sensitive Context Modeling Network for Temporal Action Localization
Xin Qin, Hanbin Zhao, Guangchen Lin +3
Temporal action localization is an important and challenging task that aims to locate temporal regions in real-world untrimmed videos where actions occur and recognize their classe…
Unsupervised Domain Adaptation for Image Classification via Structure-Conditioned Adversarial Learning
Hui Wang, Jian Tian, Songyuan Li +4
Unsupervised domain adaptation (UDA) typically carries out knowledge transfer from a label-rich source domain to an unlabeled target domain by adversarial learning. In principle, e…
Memory Efficient Class-Incremental Learning for Image Classification
Hanbin Zhao, Hui Wang, Yongjian Fu +2
With the memory-resource-limited constraints, class-incremental learning (CIL) usually suffers from the "catastrophic forgetting" problem when updating the joint classification mod…
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning
Hanbin Zhao, Hao Zeng, Xin Qin +4
As an important and challenging problem, multi-domain learning (MDL) typically seeks for a set of effective lightweight domain-specific adapter modules plugged into a common domain…
MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning
Hanbin Zhao, Yongjian Fu, Mintong Kang +3
As a challenging problem, few-shot class-incremental learning (FSCIL) continually learns a sequence of tasks, confronting the dilemma between slow forgetting of old knowledge and f…
Epoch-evolving Gaussian Process Guided Learning
Jiabao Cui, Xuewei Li, Bin Li +3
In this paper, we propose a novel learning scheme called epoch-evolving Gaussian Process Guided Learning (GPGL), which aims at characterizing the correlation information between th…