Author name cluster

De Xie

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3 papers

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AAAI Conference 2019 Conference Paper

Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval

Chao Li
Cheng Deng
Lei Wang
De Xie
Xianglong Liu

In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results in cross-modal retrieval community have been achieved. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes. Specifically, our proposed UCH seamlessly couples these two networks with generative adversarial mechanism, which can be optimized simultaneously to learn representation and hash codes. Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods.

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NeurIPS Conference 2019 Conference Paper

Cross-Modal Learning with Adversarial Samples

Chao Li
Shangqian Gao
Cheng Deng
De Xie
Wei Liu

With the rapid developments of deep neural networks, numerous deep cross-modal analysis methods have been presented and are being applied in widespread real-world applications, including healthcare and safety-critical environments. However, the recent studies on robustness and stability of deep neural networks show that a microscopic modification, known as adversarial sample, which is even imperceptible to humans, can easily fool a well-performed deep neural network and brings a new obstacle to deep cross-modal correlation exploring. In this paper, we propose a novel Cross-Modal correlation Learning with Adversarial samples, namely CMLA, which for the first time presents the existence of adversarial samples in cross-modal data. Moreover, we provide a simple yet effective adversarial sample learning method, where inter- and intra- modality similarity regularizations across different modalities are simultaneously integrated into the learning of adversarial samples. Finally, our proposed CMLA is demonstrated to be highly effective in cross-modal hashing based retrieval. Extensive experiments on two cross-modal benchmark datasets show that the adversarial examples produced by our CMLA are efficient in fooling a target deep cross-modal hashing network. On the other hand, such adversarial examples can significantly strengthen the robustness of the target network by conducting an adversarial training.

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AAAI Conference 2019 Conference Paper

Semantic Adversarial Network with Multi-Scale Pyramid Attention for Video Classification

De Xie
Cheng Deng
Hao Wang
Chao Li
Dapeng Tao

Two-stream architecture have shown strong performance in video classification task. The key idea is to learn spatiotemporal features by fusing convolutional networks spatially and temporally. However, there are some problems within such architecture. First, it relies on optical flow to model temporal information, which are often expensive to compute and store. Second, it has limited ability to capture details and local context information for video data. Third, it lacks explicit semantic guidance that greatly decrease the classification performance. In this paper, we proposed a new two-stream based deep framework for video classification to discover spatial and temporal information only from RGB frames, moreover, the multi-scale pyramid attention (MPA) layer and the semantic adversarial learning (SAL) module is introduced and integrated in our framework. The MPA enables the network capturing global and local feature to generate a comprehensive representation for video, and the SAL can make this representation gradually approximate to the real video semantics in an adversarial manner. Experimental results on two public benchmarks demonstrate our proposed methods achieves state-of-the-art results on standard video datasets.

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