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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Hanjing Jiang Jianqing Lin Huachao Zhu Yabing Huang Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Computer Science Wuhan University Wuhan China Department of Pathology Renmin Hospital of Wuhan University Wuhan China

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy.

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Down-Level control Based on Level Prediction of Landslide Evolutionary State 11

Down-Level Control Based on Level Prediction of Landslide Ev...

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11th International Conference on Information Science and Technology, ICIST 2021

作者： Sun, Shu Lian, Cheng Wang, Xiaoping Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan China Wuhan University of Technology School of Automation Wuhan China Huazhong University of Science and Technology Sch. of Artif. Intell. and Automation Key Laboratory of Image Processing and Intelligent Control Wuhan China

ISBN: (纸本)9781665412667

Accurate prediction and effective control are the key to reducing the impact of landslide disasters. In practice, most of recent studies focus on landslide displacement prediction and control. In this paper, we propose a new control method based on the level prediction of landslide evolution state, namely down-level control. The core components are level predictor and interval predictor in this method. Specially, we first transform the traditional displacement regression prediction problem into a level classification prediction problem by labeling discrete category information for landslide sample points. Then the level predictor based on Multi-task learning-Stacked long-short time memory network (MTL-SLSTM) is established to predict the state of the landslide and judge whether the system needs to be controlled. Finally, we design a safe rainfall interval predictor based on bootstrap method to obtain the safe value of control variation. The effectiveness of the proposed control method is verified on Baishuihe and Shiliushubao landslides. The results show the proposed down-level control method is valid and more intuitive. © 2021 IEEE.

关键词： Level control

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Weakly Supervised Text-based Person Re-Identification

Weakly Supervised Text-based Person Re-Identification

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International Conference on Computer Vision (ICCV)

作者： Shizhen Zhao Changxin Gao Yuanjie Shao Wei-Shi Zheng Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Sun Yat-sen University

ISBN: (纸本)9781665428132

The conventional text-based person re-identification methods heavily rely on identity annotations. However, this labeling process is costly and time-consuming. In this paper, we consider a more practical setting called weakly supervised text-based person re-identification, where only the text-image pairs are available without the requirement of annotating identities during the training phase. To this end, we propose a Cross-Modal Mutual Training (CMMT) framework. Specifically, to alleviate the intra-class variations, a clustering method is utilized to generate pseudo labels for both visual and textual instances. To further re-fine the clustering results, CMMT provides a Mutual Pseudo Label Refinement module, which leverages the clustering results in one modality to refine that in the other modality constrained by the text-image pairwise relationship. Mean-while, CMMT introduces a Text-IoU Guided Cross-Modal Projection Matching loss to resolve the cross-modal matching ambiguity problem. A Text-IoU Guided Hard Sample Mining method is also proposed for learning discriminative textual-visual joint embeddings. We conduct extensive experiments to demonstrate the effectiveness of the proposed CMMT, and the results show that CMMT performs favorably against existing text-based person re-identification methods. Our code will be available at https://***/X-BrainLab/WS_Text-ReID.

关键词： Training Visualization Computer vision Codes Annotations Computational modeling Clustering methods

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Zero-shot Text-driven Physically Interpretable Face Editing

arXiv

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arXiv 2023年

作者： Meng, Yapeng Yang, Songru Hu, Xu Zhao, Rui Li, Lincheng Shi, Zhenwei Zou, Zhengxia Image Processing Center School of Astronautics Beihang University Beijing100191 China Beijing Key Laboratory of Digital Media Beihang University Beijing100191 China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China Shanghai Artificial Intelligence Laboratory China The Netease Fuxi AI Lab Hangzhou China The Department of Guidance Navigation and Control School of Astronautics Beihang University Beijing100191 China

This paper proposes a novel and physically interpretable method for face editing based on arbitrary text prompts. Different from previous GAN-inversion-based face editing methods that manipulate the latent space of GANs, or diffusion-based methods that model image manipulation as a reverse diffusion process, we regard the face editing process as imposing vector flow fields on face images, representing the offset of spatial coordinates and color for each image pixel. Under the above-proposed paradigm, we represent the vector flow field in two ways: 1) explicitly represent the flow vectors with rasterized tensors, and 2) implicitly parameterize the flow vectors as continuous, smooth, and resolution-agnostic neural fields, by leveraging the recent advances of implicit neural representations. The flow vectors are iteratively optimized under the guidance of the pre-trained Contrastive Language-image Pretraining (CLIP) model by maximizing the correlation between the edited image and the text prompt. We also propose a learning-based one-shot face editing framework, which is fast and adaptable to any text prompt input. Our method can also be flexibly extended to real-time video face editing. Compared with state-of-the-art text-driven face editing methods, our method can generate physically interpretable face editing results with high identity consistency and image quality. Our code will be made publicly available. © 2023, CC BY-NC-SA.

关键词： Iterative methods

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Exploring Stronger Feature for Temporal Action Localization

arXiv

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arXiv 2021年

作者： Qing, Zhiwu Wang, Xiang Huang, Ziyuan Feng, Yutong Zhang, Shiwei Jiang, Jianwen Tang, Mingqian Gao, Changxin Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China

Temporal action localization aims to localize starting and ending time with action category. Limited by GPU memory, mainstream methods pre-extract features for each video. Therefore, feature quality determines the upper bound of detection performance. In this technical report, we explored classic convolution-based backbones and the recent surge of transformer-based backbones. We found that the transformer-based methods can achieve better classification performance than convolution-based, but they cannot generate accuracy action proposals. In addition, extracting features with larger frame resolution to reduce the loss of spatial information can also effectively improve the performance of temporal action localization. Finally, we achieve 42.42% in terms of mAP on validation set with a single SlowFast [9] feature by a simple combination: BMN [16]+TCANet [19], which is 1.87% higher than the result of 2020 [20]’s multi-model ensemble. Finally, we achieve Rank 1st on the CVPR2021 HACS supervised Temporal Action Localization Challenge. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolution

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OadTR: Online Action Detection with Transformers

OadTR: Online Action Detection with Transformers

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International Conference on Computer Vision (ICCV)

作者： Xiang Wang Shiwei Zhang Zhiwu Qing Yuanjie Shao Zhengrong Zuo Changxin Gao Nong Sang Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China

ISBN: (纸本)9781665428132

Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN) to capture long-range temporal structure. However, RNN suffers from non-parallelism and gradient vanishing, hence it is hard to be optimized. In this paper, we propose a new encoder-decoder framework based on Transformers, named OadTR, to tackle these problems. The encoder attached with a task token aims to capture the relationships and global inter-actions between historical observations. The decoder extracts auxiliary information by aggregating anticipated future clip representations. Therefore, OadTR can recognize current actions by encoding historical information and predicting future context simultaneously. We extensively evaluate the proposed OadTR on three challenging datasets: HDD, TVSeries, and THUMOS14. The experimental results show that OadTR achieves higher training and inference speeds than current RNN based approaches, and significantly outperforms the state-of-the-art methods in terms of both mAP and mcAP. Code is available at https://***/wangxiang1230/OadTR.

关键词： Training Visualization Recurrent neural networks Streaming media Transformers Encoding Spatiotemporal phenomena

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A pushing-grasping collaborative method based on deep q-network algorithm in dual perspectives

arXiv

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arXiv 2021年

作者： Gang, Peng Jinhu, Liao Shangbin, Guan Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Aiming at the traditional grasping method for manipulators based on 2D camera, when faced with the scene of gathering or covering, it can hardly perform well in unstructured scenes that appear as gathering and covering, for the reason that can’t recognize objects accurately in cluster scenes from a single perspective and the manipulators can’t make the environment better for grasping. In this case, a novel method of pushing-grasping collaborative based on the deep Q-network in dual perspectives is proposed in this paper. This method adopts an improved deep Q-network algorithm, with an RGB-D camera to obtain the information of objects’ RGB images and point cloud from two perspectives, and combines the pushing and grasping actions, so that the trained manipulator can make the scenes better for grasping, so that it can perform well in more complicated grasping scenes. What’s more, we improved the reward function of the deep Q-network and propose the piecewise reward function to speed up the convergence of the deep Q-network. We trained different models and tried different methods in the V-REP simulation environment, and it drew a conclusion that the method proposed in this paper converges quickly and the success rate of grasping objects in unstructured scenes raises up to 83.5%. Besides, it shows the generalization ability and well performance when novel objects appear in the scenes that the manipulator has never grasped before. Copyright © 2021, The Authors. All rights reserved.

关键词： Manipulators

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BoostTree and BoostForest for Ensemble Learning

Research Square

引用

Research Square 2021年

作者： Zhao, Changming Wu, Dongrui Huang, Jian Yuan, Ye Zhang, Hai-Tao Peng, Ruimin Shi, Zhenhua Guo, Chenfeng Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance. They have been widely used in biology, engineering, healthcare, etc. This article proposes BoostForest, which is an ensemble learning approach using BoostTree as base learners and can be used for both classification and regression. BoostTree constructs a tree model by gradient boosting. It achieves high randomness (diversity) by sampling its parameters randomly from a parameter pool, and selecting a subset of features randomly at node splitting. BoostForest further increases the randomness by bootstrapping the training data in constructing different BoostTrees. BoostForest outperformed four classical ensemble learning approaches (Random Forest, Extra-Trees, XGBoost and LightGBM) on 34 classification and regression datasets. Remarkably, BoostForest has only one hyper-parameter (the number of BoostTrees), which can be easily specified. Our code is publicly available, and the proposed ensemble learning framework can also be used to combine many other base learners. © 2021, CC BY.

关键词： Classification (of information)

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Construction of Three-Dimensional Nickel-Vanadium Hydrotalcite with Ball-Flower Architecture for Screen-Printed Asymmetric Supercapacitor

SSRN

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SSRN 2022年

作者： Tu, Qian Zhang, Qiang Sun, Xinyu Wang, Jinglu Lin, Baoying Liu, Jin Liu, Zhonghua Chen, Liangzhe School of Electronic Information Engineering Jingchu University of Technology Jingmen448000 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry Huazhong University of Science and Technology Wuhan430074 China

As a promising energy storage matter, two-dimensional (2D) layered double hydroxides (LDHs) suffer from a lower specific capacitance and poor retention. Morphology engineering is deemed to be an effective means. Herein, three-dimensional (3D) nickel-vanadium hydrotalcite (NiV-LDHs) with ball-flower structure are synthesized successfully via a facile dynamic-refluxing route, such a green strategy dispenses with template and high pressure and generates a specific surface area as high as 61.6 m 2 /g. Then, the screen-printed inks with outstanding rheological performances are formulated and the resulting electrodes display superior hydrophilic performance. Benefitted from the 3D architecture and shear flow process, the NiV-LDHs electrode can deliver an enhanced specific capacitance of 1069 F/g at 1 A/g with cycling stability of ~68.0% after 1500 cycles at 20 A/g, in contrast with that of NiV-LDHs prepared by coprecipitation (848 F/g and ~5.1%). Furthermore, an asymmetric NiV-LDHs//activated carbon supercapacitor (ASC) is assembled, which can yield a remarkable energy density of 75.8 μWh/cm2 at a power density of 0.80 mW/cm2, and two ASCs in series can illuminate a red light (2.5 V) for more than 270 s. Therefore, this study proposes a facile and economic strategy to prepare 3D nanomaterial for advanced and flexible printable energy storage devices. © 2022, The Authors. All rights reserved.

关键词： Screen printing

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Multi-Task Active Learning for Simultaneous Emotion Classification and Regression

Multi-Task Active Learning for Simultaneous Emotion Classifi...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Xue Jiang Lubin Meng Dongrui Wu Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665442084

Emotion recognition, which aims to identify an individual’s emotional state from the acquired physiological or body signals, is very important in affective computing. Emotions have two common representations: categorical, e.g., happy, sad, etc., and dimensional (continuous), e.g., valence, arousal and dominance. Training a good emotion classification or regression model usually requires a large number of labeled data. However, the labeling process is very difficult. As emotions are subtle and uncertain, it usually requires multiple assessors to label each emotional instance to obtain the groundtruth categorical label or dimensional values. In this paper, we propose a multi-task active learning (MTAL) framework to query the most useful samples for labeling, which enables the efficient training of an emotion classification model and multiple emotion regression models simultaneously. This is novel and challenging, as all previous research considered only emotion classification or regression alone, but not simultaneously. Experimental results on the IEMOCAP dataset demonstrated that MTAL outperformed random selection and several state-of-the-art single task active learning approaches, i.e., with the same number of labeled samples, MTAL can obtain better emotion classification and regression models simultaneously.

关键词： Training Emotion recognition Conferences Multitasking Physiology Data models Classification algorithms

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