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第七届中国蛋白质组学大会暨第三届国际蛋白质组学论坛

作者： Bing Yang Yan-Jie Wu Ming Zhu Jinzhong Lin Kun Zhang Shu-Kun Luo Yue-He Ding Li-Yun Xiu She Chen Keqiong Ye Si-Min He Meng-Qiu Dong National Institute of Biological Sciences BeijingBeijing 102206People''s Republic of China Key Lab of Intelligent Information ProcessingInstitute of Computing TechnologyChinese Academy of SciencesBeijing 100190People''s Republic of China

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PromptFusion: Decoupling Stability and Plasticity for Continual Learning

arXiv

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arXiv 2023年

作者： Chen, Haoran Wu, Zuxuan Han, Xintong Jia, Menglin Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Huya Inc China Cornell Univeristy United States

Current research on continual learning mainly focuses on relieving catastrophic forgetting, and most of their success is at the cost of limiting the performance of newly incoming tasks. Such a trade-off is referred to as the stability-plasticity dilemma and is a more general and challenging problem for continual learning. However, the inherent conflict between these two concepts makes it seemingly impossible to devise a satisfactory solution to both of them simultaneously. Therefore, we ask, "is it possible to divide them into two separate problems to conquer them independently?". To this end, we propose a prompt-tuning-based method termed PromptFusion to enable the decoupling of stability and plasticity. Specifically, PromptFusion consists of a carefully designed Stabilizer module that deals with catastrophic forgetting and a Booster module to learn new knowledge concurrently. Furthermore, to address the computational overhead brought by the additional architecture, we propose PromptFusion-Lite which improves PromptFusion by dynamically determining whether to activate both modules for each input image. Extensive experiments show that both PromptFusion and PromptFusion-Lite achieve promising results on popular continual learning datasets for class-incremental and domain-incremental settings. Especially on Split-Imagenet-R, one of the most challenging datasets for class-incremental learning, our method can exceed state-of-the-art prompt-based methods by more than 5% in accuracy, with PromptFusion-Lite using 14.8% less computational resources than PromptFusion. Code is available at https://***/HaoranChen/PromptFusion. © 2023, CC BY.

关键词： Contrastive Learning

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A Dynamic Demand-driven Smart Manufacturing for Mass Individualization Production

A Dynamic Demand-driven Smart Manufacturing for Mass Individ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Qiyu Hong Zhenyi Chen Chen Dong Qiancheng Xiong College of Computer and Data Science Fuzhou University Fuzhou China Key Lab of Information Security of Network Systems Fuzhou University Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University University of South Florida Tampa United States

ISBN: (纸本)9781665442084

Industry 4.0 intends to realize mass individualization production driven by customers’ demands, thus generating several advanced manufacturing modes. However, the existing modes have disadvantages such as low customer participation, low response for modifications in real-time, and low utilization of distributed idle productivity resources. The above deficiencies would not cater to the idea of Industry 4.0 well, making the cost of individualization products too high or the degree of individualization too low. In this paper, a customer highly involved dynamic demand-driven smart manufacturing mode and a mathematical model are presented for mass individualization production. Under this mode, customers can be prosumers, participating in the whole production process and proposing modifications. Meanwhile, multiple distributed idle productivity resources are employed with dynamic demand-driven manufacturing. In consequence of the mathematical model and algorithms, the multiple task allocation among multiple factories problem is solved, maximizing the total profit of available factories with high efficiency and low resource waste. Experiments and comparisons are made about the feasibility and efficiency of the algorithms.

关键词： Productivity Heuristic algorithms Dynamic scheduling Real-time systems Production facilities Mathematical models Fourth Industrial Revolution

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Color PNG Image Authentication Scheme Based on Rehashing and Secret Sharing Method

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Journal of Information Hiding and Multimedia signal processing 2015年第3期6卷 523-533页

作者： Lyu, Wan-Li Chang, Chin-Chen Wang, Feng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui University Hefei230039 China Department of Information Engineering and Computer Science Feng Chia University 100 Wenhwa Rd Seatwen Taichung40724 Taiwan Department of Mathematics and Physics Fujian University of Technology Fuzhou Fujian350108 China

An authentication scheme that uses rehashing and secret sharing methods to verify the reality of a color image’s content is proposed in this paper. First, the proposed scheme uses Shamir’s secret sharing method to share image features. Second, the proposed scheme innovatively utilizes the rehashing method to overcome easy collisions of the random numbers to enhance the effectiveness against attacks. Third, the proposed method utilizes Portable Network Graphics images to hide authentication messages. Using the rehashing method, the collisions take up little in address space, making the process suitable for processing authentication messages. The experimental results indicated that the proposed scheme had very low false acceptance and rejection ratios and verified that the rehashing method can be used in image authentication applications. © 2009 ISSN 2073-4212.

关键词： Authentication

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Structure-constrain Source Free Domain Adaptation with Regularized Knowledge Distillation of Source Model

Structure-constrain Source Free Domain Adaptation with Regul...

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Data Driven Control and Learning Systems (DDCLS)

作者： Dan Yang Xin Peng Cheng Su Dayu Tan Chaoyang Chen Weimin Zhong Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei Anhui China School of Information and Electrical Engineering Hunan University of Science and Technology Xiangtan China

ISBN: (数字)9798350361674

ISBN: (纸本)9798350361681

Transfer learning aims to leverage the knowledge of the source domain to help the learning of unlabeled target domain models. However, all conventional transfer learning methods assume that samples from source and target domains are always freely accessible. However, this assumption is rarely plausible in real-world due to data privacy. To combat this challenge, we introduce Structure-constrain Source Free Domain Adaptation with regularized Knowledge Distillation of source model (SSFDA-KD). Our method is based on the observations that a deterministic classification result can be obtained when the target data distribution aligns with the source data distribution, and a well-trained model not only yields a deterministic classification for a sample but also has clear category boundaries, which implies samples that are close together belong to the same category. From those, we first generate the source model with labeled source data, which is regarded as a well-trained model. Then, we identify target prototypes with lower self-entropy and assign the pseudo-labels to other samples according to the neighborhood structure. Finally, we refine the target model by minimizing prediction loss, constraining internal structure via triplet loss, and reducing knowledge distillation loss. Experiments of fault diagnosis based on WWTPs are conducted to validate its effectiveness.

关键词： Fault diagnosis Learning systems Adaptation models Data privacy Transfer learning Prototypes Predictive models

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DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly Detection

arXiv

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arXiv 2023年

作者： Zhang, Hui Wang, Zheng Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China School of Computer Science Zhejiang University of Technology China

Anomaly detection has garnered extensive applications in real industrial manufacturing due to its remarkable effectiveness and efficiency. However, previous generative-based models have been limited by suboptimal reconstruction quality, hampering their overall performance. We introduce DiffusionAD, a novel anomaly detection pipeline comprising a reconstruction sub-network and a segmentation sub-network. A fundamental enhancement lies in our reformulation of the reconstruction process using a diffusion model into a noise-to-norm paradigm. Here, anomalous regions are perturbed with Gaussian noise and reconstructed as normal, overcoming the limitations of previous models by facilitating anomaly-free restoration. Additionally, we propose a rapid one-step denoising paradigm, significantly faster than the traditional iterative denoising in diffusion models. Furthermore, the introduction of the norm-guided paradigm elevates the accuracy and fidelity of reconstructions. The segmentation sub-network predicts pixel-level anomaly scores using the input image and its anomaly-free restoration. Comprehensive evaluations on four standard and challenging benchmarks reveal that DiffusionAD outperforms current state-of-the-art approaches, demonstrating the effectiveness and broad applicability of the proposed pipeline. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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SegIC: Unleashing the Emergent Correspondence for In-Context Segmentation

arXiv

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arXiv 2023年

作者： Meng, Lingchen Lan, Shiyi Li, Hengduo Alvarez, Jose M. Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China NVIDIA United States University of Maryland United States

In-context segmentation aims at segmenting novel images using a few labeled example images, termed as "in-context examples", exploring content similarities between examples and the target. The resulting models can be generalized seamlessly to novel segmentation tasks, significantly reducing the labeling and training costs compared with conventional pipelines. However, in-context segmentation is more challenging than classic ones requiring the model to learn segmentation rules conditioned on a few samples. Unlike previous work with ad-hoc or non-end-to-end designs, we propose SegIC, an end-to-end segment-in-context framework built upon a single vision foundation model (VFM). In particular, SegIC leverages the emergent correspondence within VFM to capture dense relationships between target images and in-context samples. As such, information from in-context samples is then extracted into three types of instructions, i.e. geometric, visual, and meta instructions, serving as explicit conditions for the final mask prediction. SegIC is a straightforward yet effective approach that yields state-of-the-art performance on one-shot segmentation benchmarks. Notably, SegIC can be easily generalized to diverse tasks, including video object segmentation and open-vocabulary segmentation. Code will be available at https://***/MengLcool/SEGIC. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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MotionEditor: Editing Video Motion via Content-Aware Diffusion

MotionEditor: Editing Video Motion via Content-Aware Diffusi...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shuyuan Tu Qi Dai Zhi-Qi Cheng Han Hu Xintong Han Zuxuan Wu Yu-Gang Jiang Shanahai Key Lab of Intell. Info. Processing School of CS Fudan University Shanghai Collaborative Innovation Center of Intelligent Visual Computing Microsoft Research Asia Camegie Mellon University Huya Inc.

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Existing diffusion-based video editing models have made gorgeous advances for editing attributes of a source video over time but struggle to manipulate the motion information while preserving the original protagonist's appearance and background. To address this, we propose MotionEditor, the first diffusion model for video motion editing. MotionEditor incorporates a novel content-aware motion adapter into ControlNet to capture temporal motion correspondence. While ControlNet enables direct generation based on skeleton poses, it encounters challenges when modifying the source motion in the inverted noise due to contradictory signals between the noise (source) and the condition (reference). Our adapter complements Control-Net by involving source content to transfer adapted control signals seamlessly. Further, we build up a two-branch ar-chitecture (a reconstruction branch and an editing branch) with a high-fidelity attention injection mechanism facilitating branch interaction. This mechanism enables the editing branch to query the key and value from the reconstruction branch in a decoupled manner, making the editing branch retain the original background and protagonist appearance. We also propose a skeleton alignment algorithm to address the discrepancies in pose size and position. Experiments demonstrate the promising motion editing ability of MotionEditor, both qualitatively and quantitatively. To the best of our knowledge, MotionEditor is the first to use diffusion models specifically for video motion editing, considering the origin dynamic background and camera movement.

关键词： Adaptation models Computer vision Heuristic algorithms Noise Dynamics Diffusion models Controllability

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Look Before You Match: Instance Understanding Matters in Video Object Segmentation

arXiv

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arXiv 2022年

作者： Wang, Junke Chen, Dongdong Wu, Zuxuan Luo, Chong Tang, Chuanxin Dai, Xiyang Zhao, Yucheng Xie, Yujia Yuan, Lu Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center on Intelligent Visual Computing China Microsoft Cloud + AI Microsoft Research Asia China

Exploring dense matching between the current frame and past frames for long-range context modeling, memory-based methods have demonstrated impressive results in video object segmentation (VOS) recently. Nevertheless, due to the lack of instance understanding ability, the above approaches are oftentimes brittle to large appearance variations or viewpoint changes resulted from the movement of objects and cameras. In this paper, we argue that instance understanding matters in VOS, and integrating it with memory-based matching can enjoy the synergy, which is intuitively sensible from the definition of VOS task, i.e., identifying and segmenting object instances within the video. Towards this goal, we present a two-branch network for VOS, where the query-based instance segmentation (IS) branch delves into the instance details of the current frame and the VOS branch performs spatial-temporal matching with the memory bank. We employ the well-learned object queries from IS branch to inject instance-specific information into the query key, with which the instance-augmented matching is further performed. In addition, we introduce a multi-path fusion block to effectively combine the memory readout with multi-scale features from the instance segmentation decoder, which incorporates high-resolution instance-aware features to produce final segmentation results. Our method achieves state-of-the-art performance on DAVIS 2016/2017 val (92.6% and 87.1%), DAVIS 2017 test-dev (82.8%), and YouTube-VOS 2018/2019 val (86.3% and 86.3%), outperforming alternative methods by clear margins. Copyright © 2022, The Authors. All rights reserved.

关键词： Motion compensation

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Research on solving time-dependent Schrodinger equation for higher-order FDTD based on spatial filtering

Research on solving time-dependent Schrodinger equation for ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： Wenjie Ding Guoda Xie Zhixiang Huang Education of Anhui Province. Anhui Province Key Laboratory of Target Recognition and Feature Extraction (Luan Anhui 237000 China) Ministry of Education. the Key Laboratory of Intelligent Computing and Signal Processing Hefei China Key Laboratory of Electromagnetic Environmental Sensing (Hefei 230601 China) Anhui Province Anhui University Anhui university Hefei China

Combining the spatial filtering (SF) technique with FDTD(2, q) method (2 and q denote the temporal differential order and spatial differential order, respectively), a novel SF-FDTD(2, q) method with more relaxed Courant-Friedrichs-Lewy (CFL) condition is proposed and used for solving 3D time-dependent Schrödinger equation. Then, the correctness of the SF-FDTD(2, $q$ ) method is validated by calculating eigenfrequencies of the 3D quantum well model.

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