no code implementations • 28 Sep 2022 • Xintian Wu, Hanbin Zhao, Liangli Zheng, Shouhong Ding, Xi Li
Existing methods mainly extract the text information from only one sentence to represent an image and the text representation effects the quality of the generated image well.
no code implementations • 16 Mar 2022 • Hanbin Zhao, Fengyu Yang, Xinghe Fu, Xi Li
In practice, new images are usually made available in a consecutive manner, leading to a problem called Continual Semantic Segmentation (CSS).
no code implementations • 30 Jun 2021 • Hanbin Zhao, Xin Qin, Shihao Su, Yongjian Fu, Zibo Lin, Xi Li
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 continuously update new classes while maintaining the knowledge of old videos with limited storage and computing resources.
no code implementations • 28 Jun 2021 • 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 for the samples of the confusing classes.
no code implementations • 9 Mar 2021 • Xin Qin, Hanbin Zhao, Guangchen Lin, Hao Zeng, Songcen Xu, Xi Li
In this paper, we propose a temporal-position-sensitive context modeling approach to incorporate both positional and semantic information for more precise action localization.
no code implementations • 4 Mar 2021 • Hui Wang, Jian Tian, Songyuan Li, Hanbin Zhao, Qi Tian, Fei Wu, Xi Li
Unsupervised domain adaptation (UDA) typically carries out knowledge transfer from a label-rich source domain to an unlabeled target domain by adversarial learning.
no code implementations • 4 Aug 2020 • Hanbin Zhao, Hui Wang, Yongjian Fu, Fei Wu, Xi Li
To cope with the forgetting problem, many CIL methods transfer the knowledge of old classes by preserving some exemplar samples into the size-constrained memory buffer.
no code implementations • 24 Jul 2020 • Hanbin Zhao, Hao Zeng, Xin Qin, Yongjian Fu, Hui Wang, Bourahla Omar, Xi Li
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-agnostic network.
no code implementations • 28 Jun 2020 • Hanbin Zhao, Yongjian Fu, Mintong Kang, Qi Tian, Fei Wu, Xi Li
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 fast adaptation to new knowledge.
no code implementations • 25 Jun 2020 • Jiabao Cui, XueWei Li, Bin Li, Hanbin Zhao, Bourahla Omar, Xi Li
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 the batch-level distribution and the global data distribution.