scholarly journals Bi-shifting semantic auto-encoder for zero-shot learning

2021 ◽  
Vol 30 (1) ◽  
pp. 140-167
Author(s):  
Yu Wang ◽  
Keyword(s):  
Semantic Space ◽  
Visual Features ◽  
Target Domain ◽  
Projection Function ◽  
Linear Projection ◽  
Source Domain ◽  
Proposed Model ◽  
Benchmark Datasets ◽  
Projection Functions ◽  
Projection Domain

<abstract><p>Zero-shot learning aims to transfer the model of labeled seen classes in the source domain to the disjoint unseen classes without annotations in the target domain. Most existing approaches generally consider directly adopting the visual-semantic projection function learned in the source domain to the target domain without adaptation. However, due to the distribution discrepancy between the two domains, it remains challenging in dealing with the projection domain shift problem. In this work, we formulate a novel bi-shifting semantic auto-encoder to learn the semantic representations of the target instances and reinforce the generalization ability of the projection function. The encoder aims at mapping the visual features into the semantic space by leveraging the visual features of target instances and is guided by the semantic prototypes of seen classes. While two decoders manage to respectively reconstruct the original visual features in the source and target domains. Thus, our model can capture the generalized semantic characteristics related with the seen and unseen classes to alleviate the projection function problem. Furthermore, we develop an efficient algorithm by the advantage of the linear projection functions. Extensive experiments on the five benchmark datasets demonstrate the competitive performance of our proposed model.</p></abstract>

scholarly journals Joint Partial Optimal Transport for Open Set Domain Adaptation

2020 ◽  
Author(s):  
Renjun Xu ◽  
Pelen Liu ◽  
Yin Zhang ◽  
Fang Cai ◽  
Jindong Wang ◽  
...  
Keyword(s):  
Optimal Transport ◽  
Transport Model ◽  
Domain Adaptation ◽  
General Setting ◽  
Feature Space ◽  
Set Domain ◽  
Target Domain ◽  
Source Domain ◽  
Open Set ◽  
Proposed Model

Domain adaptation (DA) has achieved a resounding success to learn a good classifier by leveraging labeled data from a source domain to adapt to an unlabeled target domain. However, in a general setting when the target domain contains classes that are never observed in the source domain, namely in Open Set Domain Adaptation (OSDA), existing DA methods failed to work because of the interference of the extra unknown classes. This is a much more challenging problem, since it can easily result in negative transfer due to the mismatch between the unknown and known classes. Existing researches are susceptible to misclassification when target domain unknown samples in the feature space distributed near the decision boundary learned from the labeled source domain. To overcome this, we propose Joint Partial Optimal Transport (JPOT), fully utilizing information of not only the labeled source domain but also the discriminative representation of unknown class in the target domain. The proposed joint discriminative prototypical compactness loss can not only achieve intra-class compactness and inter-class separability, but also estimate the mean and variance of the unknown class through backpropagation, which remains intractable for previous methods due to the blindness about the structure of the unknown classes. To our best knowledge, this is the first optimal transport model for OSDA. Extensive experiments demonstrate that our proposed model can significantly boost the performance of open set domain adaptation on standard DA datasets.

scholarly journals Multimodal Feature Learning for Video Captioning

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Sujin Lee ◽  
Incheol Kim
Keyword(s):  
Neural Networks ◽  
Natural Language ◽  
Feature Learning ◽  
Visual Features ◽  
Semantic Features ◽  
Video Captioning ◽  
Video Feature ◽  
Proposed Model ◽  
Generation Network ◽  
Benchmark Datasets

Video captioning refers to the task of generating a natural language sentence that explains the content of the input video clips. This study proposes a deep neural network model for effective video captioning. Apart from visual features, the proposed model learns additionally semantic features that describe the video content effectively. In our model, visual features of the input video are extracted using convolutional neural networks such as C3D and ResNet, while semantic features are obtained using recurrent neural networks such as LSTM. In addition, our model includes an attention-based caption generation network to generate the correct natural language captions based on the multimodal video feature sequences. Various experiments, conducted with the two large benchmark datasets, Microsoft Video Description (MSVD) and Microsoft Research Video-to-Text (MSR-VTT), demonstrate the performance of the proposed model.

scholarly journals Self-Ensembling Attention Networks: Addressing Domain Shift for Semantic Segmentation

2019 ◽  
Vol 33 ◽  
pp. 5581-5588 ◽  
Author(s):  
Yonghao Xu ◽  
Bo Du ◽  
Lefei Zhang ◽  
Qian Zhang ◽  
Guoli Wang ◽  
...  
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Great Success ◽  
Learning Models ◽  
Target Domain ◽  
Attention Networks ◽  
Source Domain ◽  
Benchmark Datasets ◽  
Different Levels

Recent years have witnessed the great success of deep learning models in semantic segmentation. Nevertheless, these models may not generalize well to unseen image domains due to the phenomenon of domain shift. Since pixel-level annotations are laborious to collect, developing algorithms which can adapt labeled data from source domain to target domain is of great significance. To this end, we propose self-ensembling attention networks to reduce the domain gap between different datasets. To the best of our knowledge, the proposed method is the first attempt to introduce selfensembling model to domain adaptation for semantic segmentation, which provides a different view on how to learn domain-invariant features. Besides, since different regions in the image usually correspond to different levels of domain gap, we introduce the attention mechanism into the proposed framework to generate attention-aware features, which are further utilized to guide the calculation of consistency loss in the target domain. Experiments on two benchmark datasets demonstrate that the proposed framework can yield competitive performance compared with the state of the art methods.

scholarly journals The Image Annotation Refinement in Embedding Feature Space based on Mutual Information

2022 ◽  
Vol 16 ◽  
pp. 191-201
Author(s):  
Wei Li ◽  
Haiyu Song ◽  
Hongda Zhang ◽  
Houjie Li ◽  
Pengjie Wang
Keyword(s):  
Mutual Information ◽  
Image Annotation ◽  
State Of The Art ◽  
Feature Space ◽  
Semantic Space ◽  
Visual Features ◽  
Novel Approach ◽  
Proposed Model ◽  
Information Method ◽  
Available Information

The ever-increasing size of images has made automatic image annotation one of the most important tasks in the fields of machine learning and computer vision. Despite continuous efforts in inventing new annotation algorithms and new models, results of the state-of-the-art image annotation methods are often unsatisfactory. In this paper, to further improve annotation refinement performance, a novel approach based on weighted mutual information to automatically refine the original annotations of images is proposed. Unlike the traditional refinement model using only visual feature, the proposed model use semantic embedding to properly map labels and visual features to a meaningful semantic space. To accurately measure the relevance between the particular image and its original annotations, the proposed model utilize all available information including image-to-image, label-to-label and image-to-label. Experimental results conducted on three typical datasets show not only the validity of the refinement, but also the superiority of the proposed algorithm over existing ones. The improvement largely benefits from our proposed mutual information method and utilizing all available information.

scholarly journals Multi-Source Domain Adaptation for Visual Sentiment Classification

2020 ◽  
Vol 34 (03) ◽  
pp. 2661-2668
Author(s):  
Chuang Lin ◽  
Sicheng Zhao ◽  
Lei Meng ◽  
Tat-Seng Chua
Keyword(s):  
Domain Adaptation ◽  
Sentiment Classification ◽  
Similar Distribution ◽  
Single Source ◽  
Target Domain ◽  
Generative Adversarial Network ◽  
Source Domain ◽  
Adversarial Network ◽  
Latent Space ◽  
Benchmark Datasets

Existing domain adaptation methods on visual sentiment classification typically are investigated under the single-source scenario, where the knowledge learned from a source domain of sufficient labeled data is transferred to the target domain of loosely labeled or unlabeled data. However, in practice, data from a single source domain usually have a limited volume and can hardly cover the characteristics of the target domain. In this paper, we propose a novel multi-source domain adaptation (MDA) method, termed Multi-source Sentiment Generative Adversarial Network (MSGAN), for visual sentiment classification. To handle data from multiple source domains, it learns to find a unified sentiment latent space where data from both the source and target domains share a similar distribution. This is achieved via cycle consistent adversarial learning in an end-to-end manner. Extensive experiments conducted on four benchmark datasets demonstrate that MSGAN significantly outperforms the state-of-the-art MDA approaches for visual sentiment classification.

scholarly journals One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Networks

2019 ◽  
Author(s):  
Tao He ◽  
Yuan-Fang Li ◽  
Lianli Gao ◽  
Dongxiang Zhang ◽  
Jingkuan Song
Keyword(s):  
Adaptive Learning ◽  
Digital Data ◽  
Generative Adversarial Networks ◽  
Target Domain ◽  
Source Domain ◽  
Learning Framework ◽  
Common Space ◽  
Adversarial Networks ◽  
Shared Space ◽  
Benchmark Datasets

With the recent explosive increase of digital data, image recognition and retrieval become a critical practical application. Hashing is an effective solution to this problem, due to its low storage requirement and high query speed. However, most of past works focus on hashing in a single (source) domain. Thus, the learned hash function may not adapt well in a new (target) domain that has a large distributional difference with the source domain. In this paper, we explore an end-to-end domain adaptive learning framework that simultaneously and precisely generates discriminative hash codes and classifies target domain images. Our method encodes two domains images into a semantic common space, followed by two independent generative adversarial networks arming at crosswise reconstructing two domains’ images, reducing domain disparity and improving alignment in the shared space. We evaluate our framework on four public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.

scholarly journals A COMPARISON OF TWO STRATEGIES FOR AVOIDING NEGATIVE TRANSFER IN DOMAIN ADAPTATION BASED ON LOGISTIC REGRESSION

2018 ◽  
Vol XLII-2 ◽  
pp. 845-852 ◽  
Author(s):  
A. Paul ◽  
K. Vogt ◽  
F. Rottensteiner ◽  
J. Ostermann ◽  
C. Heipke
Keyword(s):  
Negative Transfer ◽  
Domain Adaptation ◽  
Classification Performance ◽  
Target Domain ◽  
Maximum Mean Discrepancy ◽  
Source Domain ◽  
Training Samples ◽  
Benchmark Datasets ◽  
Consistent Performance ◽  
Class Labels

In this paper we deal with the problem of measuring the similarity between training and tests datasets in the context of transfer learning (TL) for image classification. TL tries to transfer knowledge from a source domain, where labelled training samples are abundant but the data may follow a different distribution, to a target domain, where labelled training samples are scarce or even unavailable, assuming that the domains are related. Thus, the requirements w.r.t. the availability of labelled training samples in the target domain are reduced. In particular, if no labelled target data are available, it is inherently difficult to find a robust measure of relatedness between the source and target domains. This is of crucial importance for the performance of TL, because the knowledge transfer between unrelated data may lead to negative transfer, i.e. to a decrease of classification performance after transfer. We address the problem of measuring the relatedness between source and target datasets and investigate three different strategies to predict and, consequently, to avoid negative transfer in this paper. The first strategy is based on circular validation. The second strategy relies on the Maximum Mean Discrepancy (MMD) similarity metric, whereas the third one is an extension of MMD which incorporates the knowledge about the class labels in the source domain. Our method is evaluated using two different benchmark datasets. The experiments highlight the strengths and weaknesses of the investigated methods. We also show that it is possible to reduce the amount of negative transfer using these strategies for a TL method and to generate a consistent performance improvement over the whole dataset.

scholarly journals Differentially Private Correlation Alignment for Domain Adaptation

2021 ◽  
Author(s):  
Kaizhong Jin ◽  
Xiang Cheng ◽  
Jiaxi Yang ◽  
Kaiyuan Shen
Keyword(s):  
Covariance Matrix ◽  
Domain Adaptation ◽  
Estimation Method ◽  
Training Data ◽  
Target Domain ◽  
Source Domain ◽  
Feature Spaces ◽  
Benchmark Datasets ◽  
Low Dimensional ◽  
Target Data

Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. As a simple and efficient method for domain adaptation, correlation alignment transforms the distribution of the source domain by utilizing the covariance matrix of the target domain, such that a model trained on the transformed source data can be applied to the target data. However, when source and target domains come from different institutes, exchanging information between the two domains might pose a potential privacy risk. In this paper, for the first time, we propose a differentially private correlation alignment approach for domain adaptation called PRIMA, which can provide privacy guarantees for both the source and target data. In PRIMA, to relieve the performance degradation caused by perturbing the covariance matrix in high dimensional setting, we present a random subspace ensemble based covariance estimation method which splits the feature spaces of source and target data into several low dimensional subspaces. Moreover, since perturbing the covariance matrix may destroy its positive semi-definiteness, we develop a shrinking based method for the recovery of positive semi-definiteness of the covariance matrix. Experimental results on standard benchmark datasets confirm the effectiveness of our approach.

scholarly journals Graph and Autoencoder Based Feature Extraction for Zero-shot Learning

2019 ◽  
Author(s):  
Yang Liu ◽  
Deyan Xie ◽  
Quanxue Gao ◽  
Jungong Han ◽  
Shujian Wang ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Semantic Space ◽  
Low Rank ◽  
Visual Feature ◽  
Target Domain ◽  
Intrinsic Structure ◽  
Semantic Embedding ◽  
Basic Framework ◽  
Training Samples ◽  
Proposed Model

Zero-shot learning (ZSL) aims to build models to recognize novel visual categories that have no associated labelled training samples. The basic framework is to transfer knowledge from seen classes to unseen classes by learning the visual-semantic embedding. However, most of approaches do not preserve the underlying sub-manifold of samples in the embedding space. In addition, whether the mapping can precisely reconstruct the original visual feature is not investigated in-depth. In order to solve these problems, we formulate a novel framework named Graph and Autoencoder Based Feature Extraction (GAFE) to seek a low-rank mapping to preserve the sub-manifold of samples. Taking the encoder-decoder paradigm, the encoder part learns a mapping from the visual feature to the semantic space, while decoder part reconstructs the original features with the learned mapping. In addition, a graph is constructed to guarantee the learned mapping can preserve the local intrinsic structure of the data. To this end, an L21 norm sparsity constraint is imposed on the mapping to identify features relevant to the target domain. Extensive experiments on five attribute datasets demonstrate the effectiveness of the proposed model.

scholarly journals Zero Shot Learning via Low-rank Embedded Semantic AutoEncoder

2018 ◽  
Author(s):  
Yang Liu ◽  
Quanxue Gao ◽  
Jin Li ◽  
Jungong Han ◽  
Ling Shao
Keyword(s):  
State Of The Art ◽  
Feature Space ◽  
Original Data ◽  
Semantic Space ◽  
Dimensional Subspace ◽  
Low Rank ◽  
Visual Features ◽  
Visual Feature ◽  
Benchmark Datasets ◽  
Low Dimensional

Zero-shot learning (ZSL) has been widely researched and get successful in machine learning. Most existing ZSL methods aim to accurately recognize objects of unseen classes by learning a shared mapping from the feature space to a semantic space. However, such methods did not investigate in-depth whether the mapping can precisely reconstruct the original visual feature. Motivated by the fact that the data have low intrinsic dimensionality e.g. low-dimensional subspace. In this paper, we formulate a novel framework named Low-rank Embedded Semantic AutoEncoder (LESAE) to jointly seek a low-rank mapping to link visual features with their semantic representations. Taking the encoder-decoder paradigm, the encoder part aims to learn a low-rank mapping from the visual feature to the semantic space, while decoder part manages to reconstruct the original data with the learned mapping. In addition, a non-greedy iterative algorithm is adopted to solve our model. Extensive experiments on six benchmark datasets demonstrate its superiority over several state-of-the-art algorithms.

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