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Local saliency consistency-based label inference for weakly supervised salient object detection using scribble annotations

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CAAI Transactions on Intelligence technology 2024年第1期9卷 239-249页

作者： Shuo Zhao Peng Cui Jing Shen Haibo Liu School of computer science and technology Harbin University of Science and TechnologyHarbinChina School of computer science and technology Harbin Engineering UniversityHarbinChina

Recently,weak supervision has received growing attention in the field of salient object detection due to the convenience of ***,there is a large performance gap between weakly supervised and fully supervised salient object detectors because the scribble annotation can only provide very limited foreground/background ***,an intuitive idea is to infer annotations that cover more complete object and background regions for *** this end,a label inference strategy is proposed based on the assumption that pixels with similar colours and close positions should have consistent ***,k-means clustering algorithm was first performed on both colours and coordinates of original annotations,and then assigned the same labels to points having similar colours with colour cluster centres and near coordinate cluster ***,the same annotations for pixels with similar colours within each kernel neighbourhood was set *** experiments on six benchmarks demonstrate that our method can significantly improve the performance and achieve the state-of-the-art results.

关键词： label inference salient object detection weak supervision

Minimal Context-Switching Data Race Detection with Dataflow Tracking

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Journal of computer science & technology 2024年第1期39卷 211-226页

作者：郑龙李洋辛杰刘海峰郑然廖小飞金海 National Engineering Research Center for Big Data technology and System School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Services Computing technology and System Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China Cluster and Grid Computing Laboratory School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China

Data race is one of the most important concurrent anomalies in multi-threaded *** con-straint-based techniques are leveraged into race detection,which is able to find all the races that can be found by any oth-er sound race ***,this constraint-based approach has serious limitations on helping programmers analyze and understand data ***,it may report a large number of false positives due to the unrecognized dataflow propa-gation of the ***,it recommends a wide range of thread context switches to schedule the reported race(in-cluding the false one)whenever this race is exposed during the constraint-solving *** ad hoc recommendation imposes too many context switches,which complicates the data race *** address these two limitations in the state-of-the-art constraint-based race detection,this paper proposes DFTracker,an improved constraint-based race detec-tor to recommend each data race with minimal thread context ***,we reduce the false positives by ana-lyzing and tracking the dataflow in the *** this means,DFTracker thus reduces the unnecessary analysis of false race *** further propose a novel algorithm to recommend an effective race schedule with minimal thread con-text switches for each data *** experimental results on the real applications demonstrate that 1)without removing any true data race,DFTracker effectively prunes false positives by 68%in comparison with the state-of-the-art constraint-based race detector;2)DFTracker recommends as low as 2.6-8.3(4.7 on average)thread context switches per data race in the real world,which is 81.6%fewer context switches per data race than the state-of-the-art constraint based race ***,DFTracker can be used as an effective tool to understand the data race for programmers.

关键词： data race satisfiability modulo theory multi-threaded program dynamic detection

Graph Accelerators—A Case for Sparse Data Processing

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Journal of computer science & technology 2024年第2期39卷 243-244页

作者：陈文光 department of computer science and technology Tsinghua UniversityBeijing 100084China

Graph is a powerful sparse data structure that intuitively represents entities and their *** graph traversal algorithms such as Breadth-First Search(BFS),Single-Source Shortest Path(SSSP),PageRank,and Weakly Connected Components(WCC)have extensive applications in social network analysis,risk management for finance,and recommendation ***,graph processing in CPUs and GPUs is not very efficient due to its irregular memory *** people have proposed software approaches to speed up graph processing,such as PowerGraph,PowerLyra,and Shentu,which address load imbalance issues by replicating high-degree *** and GridGraph attempt to improve memory access locality by scanning the edge list of graphs while localizing the range of vertices accessed in a *** and Gemini provide adaptive dual compute modes(bottom-up and topdown),which are particularly effective for BFS-like algorithms such as BFS and ***,pure software approaches have their limitations,and it is desired to see how hardware could be employed to accelerate graph processing.

关键词： intuitive desired limitations

A Distributed Adaptive Second-Order Latent Factor Analysis Model

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2343-2345页

作者： Jialiang Wang Weiling Li Xin Luo the School of computer science and technology Dongguan University of Technology the College of computer and Information science Southwest University

Dear Editor,This letter presents a distributed adaptive second-order latent factor(DAS) model for addressing the issue of high-dimensional and incomplete data representation. Compared with first-order optimizers, a second-order optimizer has stronger ability in approaching a better solution when dealing with the non-convex optimization problems, thus obtaining better performance in extracting the latent factors(LFs) well representing the known information from high-dimensional and incomplete data.

关键词： Particle swarm optimization (PSO)

Deep Guided Attention Network for Joint Denoising and Demosaicing in Real Image

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Chinese Journal of Electronics 2024年第1期33卷 303-312页

作者： Tao ZHANG Ying FU Jun ZHANG School of computer science and technology Beijing Institute of Technology Advanced Reasearch Institute of Multidisciplinary science Beijing Institute of Technology

Denoising(DN) and demosaicing(DM) are the first crucial stages in the image signal processing pipeline. Recently, researches pay more attention to solve DN and DM in a joint manner, which is an extremely undetermined inverse problem. Existing deep learning methods learn the desired prior on synthetic dataset, which limits the generalization of learned network to the real world data. Moreover, existing methods mainly focus on the raw data property of high green information sampling rate for DM, but occasionally exploit the high intensity and signalto-noise(SNR) of green channel. In this work, a deep guided attention network(DGAN) is presented for real image joint DN and DM(JDD), which considers both high SNR and high sampling rate of green information for DN and DM, respectively. To ease the training and fully exploit the data property of green channel, we first train DN and DM sub-networks sequentially and then learn them jointly, which can alleviate the error accumulation. Besides, in order to support the real image JDD, we collect paired raw clean RGB and noisy mosaic images to conduct a realistic dataset. The experimental results on real JDD dataset show the presented approach performs better than the state-of-the-art methods, in terms of both quantitative metrics and qualitative visualization.

关键词： Training Visualization Inverse problems Green products Noise reduction Pipelines Signal processing

Multimodal Dependence Attention and Large-Scale Data Based Offline Handwritten Formula Recognition

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Journal of computer science & technology 2024年第3期39卷 654-670页

作者：刘汉超董兰芳张信明 School of computer science and technology University of Science and Technology of ChinaHefei 230022China CCF IEEE

Offline handwritten formula recognition is a challenging task due to the variety of handwritten symbols and two-dimensional formula ***,the deep neural network recognizers based on the encoder-decoder frame-work have achieved great improvements on this ***,the unsatisfactory recognition performance for formulas with long LTeX strings is one shortcoming of the existing ***,lacking sufficient training data also limits the capability of these *** this paper,we design a multimodal dependence attention(MDA)module to help the model learn visual and semantic dependencies among symbols in the same formula to improve the recognition perfor-mance of the formulas with long LTeX *** alleviate overfitting and further improve the recognition performance,we also propose a new dataset,Handwritten Formula Image Dataset(HFID),which contains 25620 handwritten formula images collected from real *** conduct extensive experiments to demonstrate the effectiveness of our proposed MDA module and HFID dataset and achieve state-of-the-art performances,63.79%and 65.24%expression accuracy on CROHME 2014 and CROHME 2016,respectively.

关键词： handwritten formula recognition multimodal dependence attention semantic dependence visual dependence Handwritten Formula Image Dataset

STDNet: A Spatio-Temporal Decomposition Neural Network for Multivariate Time Series Forecasting

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Tsinghua science and technology 2024年第4期29卷 1232-1247页

作者： Zhuolun Jiang Zefei Ning Hao Miao Li Wang College of computer science and technology(College of Data science) Taiyuan University of TechnologyJinzhong 030600China

Long-term multivariate time series forecasting is an important task in engineering applications. It helps grasp the future development trend of data in real-time, which is of great significance for a wide variety of fields. Due to the non-linear and unstable characteristics of multivariate time series, the existing methods encounter difficulties in analyzing complex high-dimensional data and capturing latent relationships between multivariates in time series, thus affecting the performance of long-term prediction. In this paper, we propose a novel time series forecasting model based on multilayer perceptron that combines spatio-temporal decomposition and doubly residual stacking, namely Spatio-Temporal Decomposition Neural Network (STDNet). We decompose the originally complex and unstable time series into two parts, temporal term and spatial term. We design temporal module based on auto-correlation mechanism to discover temporal dependencies at the sub-series level, and spatial module based on convolutional neural network and self-attention mechanism to integrate multivariate information from two dimensions, global and local, respectively. Then we integrate the results obtained from the different modules to get the final forecast. Extensive experiments on four real-world datasets show that STDNet significantly outperforms other state-of-the-art methods, which provides an effective solution for long-term time series forecasting.

关键词： time series forecasting multivariate time series spatio-temporal decomposition

On n-universal quadratic forms over dyadic local fields

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science China Mathematics 2024年第7期67卷 1481-1506页

作者： Zilong He Yong Hu School of computer science and technology Dongguan University of TechnologyDongguan523808China department of Mathematics Southern University of Science and TechnologyShenzhen518055China

Let n≥2 be an integer. We give necessary and sufficient conditions for an integral quadratic form over dyadic local fields to be n-universal by using invariants from Beli's theory of bases of norm ***, we provide a minimal set for testing n-universal quadratic forms over dyadic local fields, as an analogue of Bhargava and Hanke's 290-theorem(or Conway and Schneeberger's 15-theorem) on universal quadratic forms with integer coefficients.

关键词： integral quadratic forms n-universal quadratic forms dyadic fields 290-theorem

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Robust video question answering via contrastive cross-modality representation learning

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science China(Information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG School of Information science and technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center School of computer science and Information Engineering Hefei University of Technology Institutes of Physical science and Information technology Anhui University School of computer science and technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning