no code implementations • NeurIPS 2007 • Yuhong Guo, Dale Schuurmans
Most previous studies in active learning have focused on selecting one unlabeled instance at one time while retraining in each iteration.
no code implementations • NeurIPS 2008 • Yuhong Guo
Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and strongly suggest important underlying structures in the data.
no code implementations • NeurIPS 2010 • Yuhong Guo
Recently, batch-mode active learning has attracted a lot of attention.
no code implementations • CVPR 2013 • Xin Li, Yuhong Guo
Recently active learning has attracted a lot of attention in computer vision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis.
no code implementations • NeurIPS 2013 • Yuhong Guo
Our method is based on the assumption that useful information for the recovery of a corrupted data matrix can be gained from an uncorrupted related data matrix.
no code implementations • NeurIPS 2013 • Min Xiao, Yuhong Guo
In this paper, we propose a two-step representation learning method to bridge the feature spaces of different languages by exploiting a set of parallel bilingual documents.
no code implementations • ICCV 2015 • Xin Li, Yuhong Guo, Dale Schuurmans
Most existing zero-shot learning methods require a user to first provide a set of semantic visual attributes for each class as side information before applying a two-step prediction procedure that introduces an intermediate attribute prediction problem.
no code implementations • CVPR 2017 • Meng Ye, Yuhong Guo
The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based space to be discriminatively aligned with the attribute-based label representation vectors.
1 code implementation • 19 Apr 2018 • Meng Ye, Yuhong Guo
Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -- in the extreme case only one instance is available for each class.
1 code implementation • 27 Apr 2018 • Kongming Liang, Yuhong Guo, Hong Chang, Xilin Chen
In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection.
no code implementations • CVPR 2019 • Meng Ye, Yuhong Guo
The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, which enhance the diversity of the classifiers and facilitate information transfer to unlabeled classes.
no code implementations • 7 Aug 2018 • Meng Ye, Yuhong Guo
The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings.
Multi-Label Image Classification Multi-label zero-shot learning +1
1 code implementation • 13 May 2019 • Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, Jieping Ye
Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years.
no code implementations • 15 Sep 2019 • Yan Yan, Yuhong Guo
Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.
no code implementations • 17 Sep 2019 • Yaser Alwattar, Yuhong Guo
In this paper, we propose a novel deep multi-level attention model to address inverse visual question answering.
no code implementations • 17 Sep 2019 • Xinyuan Lu, Yuhong Guo
Automatic question generation is an important problem in natural language processing.
no code implementations • 18 Sep 2019 • Yuan Wu, Yuhong Guo
In this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC).
General Classification Multi-Domain Sentiment Classification +4
no code implementations • ICLR 2020 • Yan Yan, Yuhong Guo
Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.
1 code implementation • ICML 2020 • Vasileios Lioutas, Yuhong Guo
Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$.
Ranked #12 on Machine Translation on WMT2014 English-French
no code implementations • 29 Mar 2020 • Zhenpeng Li, Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye
However, in practice the labeled data can come from multiple source domains with different distributions.
no code implementations • ECCV 2020 • Zhen Zhao, Yuhong Guo, Haifeng Shen, Jieping Ye
In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliary task.
no code implementations • 3 Apr 2020 • Kevin Hua, Yuhong Guo
Domain adaptation aims to exploit a label-rich source domain for learning classifiers in a different label-scarce target domain.
no code implementations • 11 May 2020 • Yan Yan, Yuhong Guo
Partial label (PL) learning tackles the problem where each training instance is associated with a set of candidate labels that include both the true label and irrelevant noise labels.
no code implementations • 18 May 2020 • Bingyu Liu, Zhen Zhao, Zhenpeng Li, Jianan Jiang, Yuhong Guo, Jieping Ye
In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge.
no code implementations • 8 Jun 2020 • Zhen Zhao, Bingyu Liu, Yuhong Guo, Jieping Ye
In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning (CD-FSL) challenge.
1 code implementation • 8 Jun 2020 • Jianan Jiang, Zhenpeng Li, Yuhong Guo, Jieping Ye
The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i. e, an instance-wise global classification network based on semantic information, after the common feature embedding network.
no code implementations • 14 Nov 2020 • Zhen Zhao, Yuhong Guo, Jieping Ye
Recently the problem of cross-domain object detection has started drawing attention in the computer vision community.
no code implementations • 3 Dec 2020 • Zhenpeng Li, Jianan Jiang, Yuhong Guo, Tiantian Tang, Chengxiang Zhuo, Jieping Ye
In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption.
no code implementations • 7 Dec 2020 • Bingyu Liu, Yuhong Guo, Jieping Ye, Weihong Deng
Inspired by the effectiveness of pseudo-labels in domain adaptation, we propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation.
no code implementations • 29 Jun 2021 • Hanping Zhang, Yuhong Guo
In this work, we propose a novel policy-aware adversarial data augmentation method to augment the standard policy learning method with automatically generated trajectory data.
no code implementations • 29 Jun 2021 • Abdullah Alchihabi, Yuhong Guo
In this paper, we propose a novel Dual GNN learning framework to address this challenge task.
no code implementations • 3 Dec 2021 • Xuejun Han, Yuhong Guo
To address this shortcoming, continual machine learners are elaborated to commendably learn a stream of tasks with domain and class shifts among different tasks.
no code implementations • 3 Apr 2022 • Qing En, Yuhong Guo
Medical image annotation typically requires expert knowledge and hence incurs time-consuming and expensive data annotation costs.
no code implementations • 10 Sep 2022 • Hanping Zhang, Yuhong Guo
As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL).
no code implementations • 15 Dec 2022 • Hao Yan, Yuhong Guo
We first split the unlabeled training set in the target domain into a pseudo-labeled confident subset and an unlabeled less-confident subset according to the prediction confidence scores from the pre-trained source model.
Source-Free Domain Adaptation Unsupervised Domain Adaptation
no code implementations • 17 Dec 2022 • Qing En, Yuhong Guo
The proposed method trains the base segmentation network by using a novel contrastive variance (CV) loss to exploit the unlabeled pixels and a partial cross-entropy loss on the labeled pixels.
no code implementations • CVPR 2023 • Taoseef Ishtiak, Qing En, Yuhong Guo
Moreover, a new exemplar embedding contrastive module is designed to enhance the discriminative capability of the segmentation model by exploiting the contrastive exemplar-based guidance knowledge in the embedding space.
no code implementations • 3 May 2023 • Xuejun Han, Yuhong Guo
In view of this, in this paper we tackle a challenging and practical continual learning scenario named few-shot class-incremental learning (FSCIL), in which labeled data are given for classes in a base session but very limited labeled instances are available for new incremental classes.
no code implementations • 18 Sep 2023 • Abdullah Alchihabi, Qing En, Yuhong Guo
As a result, instead of using the dense adjacency matrix directly, ELR-GNN can learn a low-rank and sparse estimate of it in a simple, efficient and easy to optimize manner.
no code implementations • 18 Sep 2023 • Abdullah Alchihabi, Yuhong Guo
In this work, we propose a novel mixup-based graph augmentation method, Graph Dual Mixup (GDM), that leverages both functional and structural information of the graph instances to generate new labeled graph samples.
no code implementations • 6 Dec 2023 • Abdullah Alchihabi, Marzi Heidari, Yuhong Guo
Due to the availability of only a few labeled instances for the novel target prediction task and the significant domain shift between the well annotated source domain and the target domain, cross-domain few-shot learning (CDFSL) induces a very challenging adaptation problem.
no code implementations • 17 Apr 2024 • Marzi Heidari, Hanping Zhang, Yuhong Guo
In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL).
no code implementations • 17 Apr 2024 • Hao Yan, Yuhong Guo
To address these two inherent challenges in supervised federated learning, we propose a novel lightweight unsupervised federated learning approach that leverages unlabeled data on each client to perform lightweight model training and communication by harnessing pretrained vision-language models, such as CLIP.
no code implementations • 17 Apr 2024 • Qing En, Yuhong Guo
It can learn statistical information and capture spatial correlations between image and text attributes in the embedding space, iteratively refining the mask to enhance segmentation.
no code implementations • 18 Apr 2024 • Qing En, Yuhong Guo
In this paper, we introduce a novel Cross-model Mutual learning framework for Exemplar-based Medical image Segmentation (CMEMS), which leverages two models to mutually excavate implicit information from unlabeled data at multiple granularities.