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Ke Han

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3 papers

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AAAI Conference 2022 Conference Paper

Generalizable Person Re-identification via Self-Supervised Batch Norm Test-Time Adaption

Ke Han
Chenyang Si
Yan Huang
Liang Wang
Tieniu Tan

In this paper, we investigate the generalization problem of person re-identification (re-id), whose major challenge is the distribution shift on an unseen domain. As an important tool of regularizing the distribution, batch normalization (BN) has been widely used in existing methods. However, they neglect that BN is severely biased to the training domain and inevitably suffers the performance drop if directly generalized without being updated. To tackle this issue, we propose Batch Norm Test-time Adaption (BNTA), a novel re-id framework that applies the self-supervised strategy to update BN parameters adaptively. Specifically, BNTA quickly explores the domain-aware information within unlabeled target data before inference, and accordingly modulates the feature distribution normalized by BN to adapt to the target domain. This is accomplished by two designed self-supervised auxiliary tasks, namely part positioning and part nearest neighbor matching, which help the model mine the domain-aware information with respect to the structure and identity of body parts, respectively. To demonstrate the effectiveness of our method, we conduct extensive experiments on three re-id datasets and confirm the superior performance to the stateof-the-art methods.

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JBHI Journal 2022 Journal Article

Mul-SNO: A Novel Prediction Tool for S-Nitrosylation Sites Based on Deep Learning Methods

Qian Zhao
Jiaqi Ma
Yu Wang
Fang Xie
Zhibin Lv
Yaoqun Xu
Hua Shi
Ke Han

Protein s-nitrosylation (SNO) is one of the most important post-translational modifications and is formed by the covalent modification of nitric oxide and cysteine residues. Extensive studies have shown that SNO plays a pivotal role in the plant immune response and treating various major human diseases. In recent years, SNO sites have become a hot research topic. Traditional biochemical methods for SNO site identification are time-consuming and costly. In this study, we developed an economical and efficient SNO site prediction tool named Mul-SNO. Mul-SNO ensembled current popular and powerful deep learning model bidirectional long short-term memory (BiLSTM) and bidirectional encoder representations from Transformers (BERT). Compared with existing state-of-the-art methods, Mul-SNO obtained better ACC of 0. 911 and 0. 796 based on 10-fold cross-validation and independent data sets, respectively.

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AAAI Conference 2020 Conference Paper

Feature Deformation Meta-Networks in Image Captioning of Novel Objects

Tingjia Cao
Ke Han
Xiaomei Wang
Lin Ma
Yanwei Fu
Yu-Gang Jiang
Xiangyang Xue

This paper studies the task of image captioning with novel objects, which only exist in testing images. Intrinsically, this task can reﬂect the generalization ability of models in understanding and captioning the semantic meanings of visual concepts and objects unseen in training set, sharing the similarity to one/zero-shot learning. The critical difﬁculty thus comes from that no paired images and sentences of the novel objects can be used to help train the captioning model. Inspired by recent work (Chen et al. 2019b) that boosts one-shot learning by learning to generate various image deformations, we propose learning meta-networks for deforming features for novel object captioning. To this end, we introduce the feature deformation meta-networks (FDM-net), which is trained on source data, and learn to adapt to the novel object features detected by the auxiliary detection model. FDM-net includes two sub-nets: feature deformation, and scene graph sentence reconstruction, which produce the augmented image features and corresponding sentences, respectively. Thus, rather than directly deforming images, FDM-net can efﬁciently and dynamically enlarge the paired images and texts by learning to deform image features. Extensive experiments are conducted on the widely used novel object captioning dataset, and the results show the effectiveness of our FDM-net. Ablation study and qualitative visualization further give insights of our model.

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