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dstagnn's Introduction

Shiyong Lan -Homepage

https://cs.scu.edu.cn/info/1280/13694.htm

招收计算机视觉多模态感知理解数据分析方面的研究生,欢迎报考四川大学计算机学院(304)和 视觉合成图形图像技术国防重点学科实验室(604)的同学联系

Publications selected:

[17]. Semantic-aware normalizing flow with feature fusion for image anomaly detection, Neurocomputing 590 (2024), 127728. --[paper]. [code]

[16]. A Semantics-aware Normalizing Flow Model for Anomaly Detection, The IEEE International Conference on Multimedia & Expo (ICME) 2023.--[pdf]. [code]

[15]. Visual-Haptic-Kinesthetic Object Recognition with Multimodal Transformer. International Conference on Artificial Neural Networks (ICANN2023)--[pdf]. [code].

[14]. Siamese Network based on MLP and Multi-head Cross Attention for Visual Object Tracking. International Conference on Artificial Neural Networks (ICANN2023)--[pdf]. [code].

[13]. GanNeXt: A New Convolutional GAN for Anomaly Detection. International Conference on Artificial Neural Networks (ICANN2023)--[pdf]. [code].

[12]. DLAHSD: Dynamic Label adopted in Auxiliary Head for SAR Detection. International Conference on Image Processing (ICIP 2023) --[pdf]. [code].

[11]. Flow-based one-class anomaly detection with Multi-frequency Feature fusion. International Conference on Image Processing (ICIP 2023) --[pdf]. [code].

[10]. DSTAGNN: Dynamic Spatial-Temporal Aware Graph Neural Network for Traffic Flow Forecasting. International Conference on Machine Learning (ICML) 2022, PMLR 162:11906-11917.(CCF-A) (Acceptance rate: 21.9%). [pdf] [code].

[9]. Face Super-Resolution with Spatial Attention Guided by Multiscale Receptive-Field Features. International Conference on Artificial Neural Networks (ICANN2022). Springer, Cham, 2022: 145-157. [pdf] (code).

[8]. A Transformer-based GAN for Anomaly Detection. International Conference on Artificial Neural Networks (ICANN2022). Springer, Cham, 2022: 345-357. [pdf](code).

[7]. Robust Visual Object Tracking with Spatiotemporal Regularisation and Discriminative Occlusion Deformation. International Conference on Image Processing (ICIP). IEEE, 2021, pp. 1879-1883. [pdf](code).

[6]. SAGAN: Skip-Attention GAN for Anomaly Detection, International Conference on Image Processing (ICIP). IEEE, 2021, pp. 2468-2472. [pdf](code).

[5]. Real-Time 3D Road Scene Based on Virtual-Real Fusion Method. IEEE Sensors Journal, 2015, 15(2):750-756.

[4]. 3D Road Scene Monitoring Based on Real-Time Panorama. Journal of Applied Mathematics, 2014, 2014:1-8.

[3]. Effect of Slopes in Highway on Traffic Flow. International Journal of Modern Physics C, 2011, 22, 319-331.

[2].《城市应急交通流仿真建模与模拟》,中国科学技术大学出版社, 2021,ISBN:978-7-312-05329-0.(专著).

[1].《机动车号牌自动识别系统》国家标准 GB/T 28649-2012.

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dstagnn's Issues

HELP! CUDA out of memory. Tried to allocate 12.00 MiB (GPU 0; 2.00 GiB total capacity; 1.68 GiB already allocated; 0 bytes free; 1.71 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

error:
CUDA out of memory. Tried to allocate 12.00 MiB (GPU 0; 2.00 GiB total capacity; 1.68 GiB already allocated; 0 bytes free; 1.71 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

generate dataset problem

Hello, author, first of all, thank you for your hard work. I have a question to ask you. This problem occurs when the stag_001 file is generated. How did you solve it?
image

Problems during training

Hello, when I execute your training file, the program always stays at the position shown in the figure. What is the reason for this? Is the saving slow because of the large number of parameters?
I0FDV00G6ITHNJ)()S(I%UM

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