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49    <a class="navbar-brand" href="/">Shuguang Dou</a>
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53		<li><a href="/">Home</a></li>
54		<li><a href="/publications">Publications</a></li>
55		<li><a href="/codes">Codes</a></li>
56		<li><a href="/experience">Experience</a></li>
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72      <p>I am a Researcher at Algorithm Novelty Lab of Huawei Tech. I obtained my Ph.D. degree from the School of Computer Science and Technology, Tongji University.
73During my PhD studies, I collaborated with researchers <a href="https://scholar.google.com/citations?user=JiTfWVMAAAAJ">Xinyang Jiang</a> and <a href="http://recmind.cn/">Dongsheng Li</a> in theat Microsoft Research Asia (MSRA). 
74While interning at MSRA, my research focused on infographic understanding and neural architecture search (NAS) benchmarks. 
75I am also a visiting scholar luckily invited by <a href="https://www.cse.msu.edu/~liuxm/index2.html">Prof. Xiaoming Liu</a> of MSU. During my doctoral studies, my research primarily focused on human-centric video understanding, with a specific emphasis on trustworthy person re-identification (ReID). My work addressed the critical pillars of robustness, security, and privacy preservation within this domain. Currently, I am focused on advancing research in the following areas:</p>
76      <ol>
77  <li>Agentic Vision</li>
78  <li>Distillation of Model and Dataset </li>
79  <li>Medical Image Analysis</li>
80</ol>
81
82      <p><strong>We are hiring research scientists, engineers, and research interns for our lab! Please <a href="mailto:[email protected]">drop me a line</a> if you are interested.</strong></p>
83
84      <h3 id="cv">CV</h3>
85      <p>You can download my CV at <a href="https://shuguang-52.github.io/papers/Resume_ShuguangDou.pdf">here</a>.</p>
86
87      <h3 id="news">News</h3>
88
89      <p>May 1, 2026 :
90<em>A paper on ‘Distilling and Scaling Hierarchical Vision Transformer to 30B Parameters’ is accepted by IJCAI 2026.</em></p>
91
92      <p>Mar 23, 2026 :
93<em>A paper on ‘Decouple Re-identification and Human Parsing for Occluded Person Re-Identification’ is accepted by IEEE TBOIM in 2026.</em></p>
94
95      <p>Feb 21, 2026 :
96<em>A paper on ‘Agentic Video Summarization via Self-Reflecting Multimodal Understanding’ is accepted by CVPR 2026. Congratulations to my intern Miaotian Guo!</em></p>
97
98      <p>Feb 05, 2026 :
99<em>A paper on ‘Active Dataset Distillation via Dual-Space Informative Matching’ is accepted by IEEE TIP in 2026.</em></p>
100
101      <p>Jan 26, 2026 :
102<em>A paper on ‘Asynchronous Matching with Dynamic Sampling for Multimodal Dataset Distillation’ is accepted by ICLR 2026.</em></p>
103
104      <p>Jan 24, 2026 :
105<em>I have been invited to be a Reviewer for the ECCV 2026 Conference.</em></p>
106
107      <p>Jan 12, 2026 :
108<em>I have been invited to be a Reviewer for the ICML 2026 Conference.</em></p>
109
110      <p>Nov 23, 2025 :
111<em>I have been invited to be a Reviewer for the CVPR 2026 Conference.</em></p>
112
113      <p><a href="/allnews.html">see all news</a></p>
114
115    </div>
116
117    <div class="col-sm-4" style="display:table-cell; vertical-align:middle; text-align:center">
118
119      <ul style="overflow: hidden">
120  <img src="/images/profiel_pic.jpg" class="img-responsive" width="100%" />
121  </ul>
122
123      <p><!-- <br clear="all" /> --></p>
124
125      <p><a href="mailto:[email protected]">[email protected]</a> <br />
126  <a href="https://scholar.google.com.hk/citations?user=n2YT06EAAAAJ&amp;hl=zh-CN">Google Scholar</a><br />
127  SH021, Shanghai, China.<br /></p>
128
129    </div>
130
131  </div>
132</div>
133
134<div class="col-sm-12">
135
136  <h3 id="publications">Publications</h3>
137
138  <div class="col-sm-11 clearfix">
139    <div class="well">
140      <pubtit>EA-HAS-Bench and Language-Enhanced Shrinkage Search for Energy-aware NAS</pubtit>
141
142      <p><img src="/images/pubpic/25_TPAMI_LESS.png" class="img-responsive" width="200px" style="float: left" /></p>
143
144      <p>Recent studies have started integrating large language models (LLMs) into AutoML frameworks to enhance model search efficie
144ncy and configuration prediction, yet challenges remain in adapting these methods for energy-efficient searches across vast configuration spaces, as they often neglect energy consumption metrics. As a result, we introduce the Language-Enhanced Shrinkage Search (LESS), a plug-and-play method that utilizes the analytical capabilities of LLMs to enhance the energy efficiency of existing hyperparameter optimization techniques.</p>
145
146      <p><em>S<strong>huguang Dou</strong>, Jiale Zhao, Xinyang Jiang, Junyao Gao, Yuge Zhang, Bo Li, Dongsheng Li</em></p>
147
148      <p>IEEE Transactions on Pattern Analysis and Machine Intelligence (<strong>T-PAMI</strong>) in 2025, CCF A</p>
149
150      <p><a href="">pdf</a>
151 /
152 <a href="">project page</a>
153 /
154 <a href="https://ieeexplore.ieee.org/abstract/document/11122892">Website Online</a></p>
155
156    </div>
157  </div>
158
159  <div class="col-sm-11 clearfix">
160    <div class="well">
161      <pubtit>Dpl++:Advancing the network performance via image and label perturbations</pubtit>
162
163      <p><img src="/images/pubpic/25_TPAMI_DPL.png" class="img-responsive" width="200px" style="float: left" /></p>
164
165      <p>We propose a novel paradigm that leverages the benefits of image perturbations for rectifying data distributions. Our method, called DPL (Deep Perturbation Learning), introduces new insights into utilizing image perturbations and focuses on improving generalizability on normal samples, rather than resisting adversarial attacks.</p>
166
167      <p><em>Ding Qi, Jian Li, Junyao Gao, <strong>Shuguang Dou</strong>, Ying Tai, Jianlong Hu, Bo Zhao, Yabiao Wang, Chengjie Wang, Cairong Zhao</em></p>
168
169      <p>IEEE Transactions on Pattern Analysis and Machine Intelligence (<strong>T-PAMI</strong>) in 2025, CCF A</p>
170
171      <p><a href="">pdf</a>
172 /
173 <a href="">project page</a>
174 /
175 <a href="https://ieeexplore.ieee.org/abstract/document/11106184/">Website Online</a></p>
176
177    </div>
178  </div>
179
180  <div class="col-sm-11 clearfix">
181    <div class="well">
182      <pubtit>MTA-VPS:A Large-scale Benchmark for Video-Based Person Search</pubtit>
183
184      <p><img src="/images/pubpic/25_CVPRW_VPS.png" class="img-responsive" width="200px" style="float: left" /></p>
185
186      <p>We introduce Video-based Person Search (VPS), a task that tracks individuals and their trajectories in raw video scenes, enhancing performance in practical applications. Due to the lack of suitable datasets and privacy concerns, we created the first VPS dataset using virtual environments, featuring diverse continuous video scenes. Paired with an evaluation framework, this dataset assesses both retrieval performance and trajectory quality.</p>
187
188      <p><em>Ding Qi, <strong>Shuguang Dou</strong>, Jian Liu, Huaixuan Cao, Hao Zhang, Dongsheng Jiang, Cairong Zhao</em></p>
189
190      <p>Proceedings of the Computer Vision and Pattern Recognition Conference Workshop in 2025</p>
191
192      <p><a href="">pdf</a>
193 /
194 <a href="https://github.com/Vill-Lab/MTA-VPS">Dataset</a>
195 /
196 <a href="https://openaccess.thecvf.com/content/CVPR2025W/PVUW/html/Qi_MTA-VPS_A_Large-scale_Benchmark_for_Video-Based_Person_Search_CVPRW_2025_paper.html">Website Online</a></p>
197
198    </div>
199  </div>
200
201  <div class="col-sm-11 clearfix">
202    <div class="well">
203      <pubtit>Towards Universal Dataset Distillation via Task-Driven Diffusion</pubtit>
204
205      <p><img src="/images/pubpic/25_CVPR_UniDD.png" class="img-responsive" width="200px" style="float: left" /></p>
206
207      <p>We propose a universal dataset distillation framework, named UniDD, a task-driven diffusion model for diverse DD task</p>
208
209      <p><em>Ding Qi, Jian Li, Junyao Gao, <strong>Shuguang Dou</strong>, Ying Tai, Jianlong Hu, Bo Zhao, Yabiao Wang, Chengjie Wang, Cairong Zhao</em></p>
210
211      <p>Proceedings of the Computer Vision and Pattern Recognition Conference (<strong>CVPR Oral</strong>) in 2025</p>
212
213      <p><a href="">pdf</a>
214 /
215 <a href="">project page</a>
216 /
217 <a href="https://openaccess.thecvf.com/content/CVPR2025/html/Qi_Towards_Universal_Dataset_Distillation_via_Task-Driven_Diffusion_CVPR_2025_paper.html">Website Online</a></p>
218
219    </div>
220  </div>
221
222  <div class="col-sm-11 clearfix">
223    <div class="well">
224      <pubtit>One for All:A Review of Large Pre-training Models for Re-Identification</pubtit>
225
226      <p><img src="/images/pubpic/25_WWW_MORE.png" class="img-responsive" width="200px" style="float: left" /></p>
227
228      <p>In this paper, we present a comprehensive survey of large pre-training models for ReID, which we refer to as the “One for All” approach. This paradigm leverages large-scale pre-training models, such as self-supervised or language-image pre-training models, as general feature extractors adaptable to multiple ReID tasks.</p>
229
230      <p><em>Yubing Sun, <strong>Shuguang Dou</strong>, Ding Qi, Yanping Li, Jiale Zhao and Cairong Zhao</em></p>
231
232      <p>Web Conference Workshop on Multimedia Object Re-ID:Advancements, Challenges, and Opportunities (MORE 2025)</p>
233
234      <p><a href="">pdf</a>
235 /
236 <a href="">project page</a>
237 /
238 <a href="https:// github.com/ Vill-Lab/ Awesome-Evolving-ReID">Paper List (GitHub)</a>
239 /
240 <a href="https://dl.acm.org/doi/abs/10.1145/3701716.3717652">Website Online</a></p>
241
242    </div>
243  </div>
244
245  <div class="col-sm-11 clearfix">
246    <div class="well">
247      <pubtit>Fetch and Forge:Efficient Dataset Condensation for Object Detection</pubtit>
248
249      <p><img src="/images/pubpic/24_NIPS_DCOD.png" class="img-responsive" width="200px" style="float: left" /></p>
250
251      <p>
251Current research on DC mainly focuses on image classification, with less exploration of object detection. As a remedy, we propose DCOD, the first dataset condensation framework for object detection. It initially storing key localization and classification information into model parameters, and then reconstructing synthetic images via model inversion.</p>
252
253      <p><em>Ding Qi, Jian Li, Jinlong Peng, <strong>Shuguang Dou</strong>, Jialin Li, Bo Zhao, Jiangning Zhang, Yabiao Wang, Chengjie Wang, Cairong Zhao</em></p>
254
255      <p>Neural Information Processing Systems (<strong>NeuPIS</strong>) in 2024, CCF A</p>
256
257      <p><a href="">pdf</a>
258 /
259 <a href="">project page</a>
260 /
261 <a href="">code (GitHub)</a>
262 /
263 <a href="">Website Online</a></p>
264
265    </div>
266  </div>
267
268  <div class="col-sm-11 clearfix">
269    <div class="well">
270      <pubtit>Re-ID-leak:Membership Inference Attacks Against Person Re-identification</pubtit>
271
272      <p><img src="/images/pubpic/24_IJCV_ReIDLeak.png" class="img-responsive" width="200px" style="float: left" /></p>
273
274      <p>Most existing MI attack methods focus on classification models, while Re-ID follows a distinct paradigm for training and inference. Re-ID is a fine-grained recognition task that involves complex feature embedding, and the model outputs commonly used by existing MI algorithms, such as logits and losses, are inaccessible during inference. We propose a novel MI attack method based on the distribution of inter-sample similarity, which involves sampling a set of anchor images to represent the similarity  distribution that is conditioned on a target image.</p>
275
276      <p><em>Junyao Gao, Xinyang Jiang, <strong>Shuguang Dou</strong>, Dongsheng Li, Duoqian Miao, Cairong Zhao</em></p>
277
278      <p>International Journal of Computer Vision (<strong>IJCV</strong>) in 2024, CCF A</p>
279
280      <p><a href="">pdf</a>
281 /
282 <a href="https://jeoyal.github.io/home/">project page</a>
283 /
284 <a href="">code (GitHub)</a>
285 /
286 <a href="https://link.springer.com/article/10.1007/s11263-024-02115-6">Website Online</a></p>
287
288    </div>
289  </div>
290
291  <div class="col-sm-11 clearfix">
292    <div class="well">
293      <pubtit>Reviving static charts into live charts</pubtit>
294
295      <p><img src="/images/pubpic/24_TVCG_LiveChart.png" class="img-responsive" width="200px" style="float: left" /></p>
296
297      <p>Data charts are prevalent across various fields due to their efficacy in conveying complex data relationships. However, static charts may sometimes struggle to engage readers and efficiently present intricate information, potentially resulting in limited understanding. We introduce “Live Charts,” a new format of presentation that decomposes complex information within a chart and explains the information pieces sequentially through rich animations and accompanying audio narration.</p>
298
299      <p><em>Lu Ying, Yun Wang, Haotian Li, <strong>Shuguang Dou</strong>, Haidong Zhang, Xinyang Jiang, Huamin Qu, Yingcai Wu</em></p>
300
301      <p>IEEE Transactions on Visualization and Computer Graphics (<strong>T-VCG</strong>) in 2024, CCF A</p>
302
303      <p><a href="">pdf</a>
304 /
305 <a href="https://yinglu.notion.site/Hi-I-am-Lu-Ying-6ada6350f92a4daa8cc75d07f20ec81b">project page</a>
306 /
307 <a href="">code (GitHub)</a>
308 /
309 <a href="https://ieeexplore.ieee.org/abstract/document/10530507">Website Online</a></p>
310
311    </div>
312  </div>
313
314  <div class="col-sm-11 clearfix">
315    <div class="well">
316      <pubtit>Hierarchically Recognizing Vector Graphics and A New Chart-based Vector Graphics Dataset</pubtit>
317
318      <p><img src="/images/pubpic/24_TPAMI_Yolat++.png" class="img-responsive" width="200px" style="float: left" /></p>
319
320      <p>The conventional approach to image recognition has been based on raster graphics, which can suffer from aliasing and information loss when scaled up or down. In this paper, we propose a novel approach that leverages the benefits of vector graphics for object localization and classification. Our method, called YOLaT (You Only Look at Text), takes the textual document of vector graphics as input, rather than rendering it into pixels. We propose YOLaT++ to learn Multi-level Abstraction Feature Learning from Primitive Shapes to Curves and Points. On the other hand, given few public datasets focus on vector graphics, data-driven learning cannot exert its full power on this format. We provide a large-scale and challenging dataset for Chart-based Vector Graphics Detection and Chart Understanding, termed VG-DCU, with vector graphics, raster graphics, annotations, and raw data drawn for creating these vector charts.</p>
321
322      <p><em>S<strong>huguang Dou</strong>, Xinyang Jiang, Lu Liu, Lu Ying, Caihua Shan, Yifei Shen, Xuanyi Dong, Yun Wang, Dongsheng Li, Cairong Zhao</em></p>
323
324      <p>IEEE Transactions on Pattern Analysis and Machine Intelligence (<strong>T-PAMI</strong>) in 2024, CCF A</p>
325
326      <p><a href="">pdf</a>
327 /
328 <a href="http://shuguang-52.github.io/vgdcu">project page</a>
329 /
330 <a href="https://github.com/microsoft/YOLaT-VectorGraphicsRecognition">code (GitHub)</a>
331 /
332 <a href="https://ieeexplore.ieee.org/abstract/document/10508965">Website Online</a></p>
333
334    </div>
335  </div>
336
337  <div class="col-sm-11 clearfix">
338    <div class="well">
339      <pubtit>Adaptive Discriminative Regularization for Visual Classification</pubtit>
340
341      <p><img src="/images/pubpic/24_IJCV_ADR.png" class="img-responsive" width="200px" style="float: left" /></p>
342
343      <p>How to improve discriminative feature learning is central in classification. In this paper, we embrace the real-world data distribution setting in that some classes share semantic overlaps due to their similar appearances or concepts. Regarding this hypothesis, we propose a novel regularization to improve discriminative learning. We first calibrate the estimated highest likelihood of one sample based on its semantically neighboring classes, then encourage the overall likelihood predictions to be deterministic by imposing an adaptive exponential penalty.</p>
344
345      <p><em>Qingsong Zhao, Yi Wang, <strong>Shuguang Dou</strong>, Chen Gong, Yin Wang and Cairong Zhao</em></p>
346
347      <p>International Journal of Computer Vision (<strong>IJCV</strong>) in 2024, CCF A</p>
348
349      <p><a href="https://shuguang-52.github.io/papers/24ijcv_adr.pdf">pdf</a>
350 /
351 <a href="https://zqsiat.github.io/">project page</a>
352 /
353 <a href="">code (GitHub)</a>
354 /
355 <a href="https://link.springer.com/article/10.1007/s11263-024-02080-0?utm_source=rct_congratemailt&amp;utm_medium=email&amp;utm_campaign=nonoa_20240513&amp;utm_content=10.1007/s11263-024-02080-0">Website Online</a></p>
356
357    </div>
358  </div>
359
360  <div class="col-sm-11 clearfix">
361    <div class="well">
362      <pubtit>Invisible backdoor attack with dynamic triggers against person re-identification</pubtit>
363
364      <p><img src="/images/pubpic/23_TIFS_backdoor.png" class="img-responsive" width="200px" style="float: left" /></p>
365
366      <p>Existing backdoor attack methods follow an all-to-one or all-to-all attack scenario, where all the target classes in the test set have already been seen in the training set. However, ReID is a much more complex fine-grained open-set recognition problem, where the identities in the test set are not contained in the training set. To ameliorate this issue, we propose a novel backdoor attack on deep ReID under a new all-to-unknown scenario, called Dynamic Triggers Invisible Backdoor Attack (DT-IBA). Instead of learning fixed triggers for the target classes from the training set, DT-IBA can dynamically generate new triggers for any unknown identities.</p>
367
368      <p><em>Wenli Sun, Xinyang Jiang, <strong>Shuguang Dou</strong>, Dongsheng Li, Duoqian Miao, Cheng Deng, Cairong Zhao</em></p>
369
370      <p>
370IEEE Transactions on Information Forensics and Security (<strong>T-IFS</strong>) in 2023, CCF A</p>
371
372      <p><a href="https://shuguang-52.github.io/papers/23tifs_backdoor.pdf">pdf</a>
373 /
374 <a href="http://shuguang-52.github.io/backdoor">project page</a>
375 /
376 <a href="https://github.com/Vill-Lab/2023-TIFS-DTIBA">code (GitHub)</a>
377 /
378 <a href="https://ieeexplore.ieee.org/abstract/document/10285514/">Website Online</a></p>
379
380    </div>
381  </div>
382
383  <div class="col-sm-11 clearfix">
384    <div class="well">
385      <pubtit>EA-HAS-Bench:Energy-Aware Hyperparameter and Architecture Search Benchmark</pubtit>
386
387      <p><img src="/images/pubpic/23_ICLR_BSC.png" class="img-responsive" width="200px" style="float: left" /></p>
388
389      <p>We present the first large-scale energy-aware benchmark that allows studying AutoML methods to achieve better trade-offs between performance and search energy consumption, named EA-HAS-Bench. EA-HAS-Bench provides a large-scale architecture/hyperparameter joint search space, covering diversified configurations related to energy consumption.</p>
390
391      <p><em>S<strong>huguang Dou</strong>, Xinyang Jiang, Cairong Zhao, Dongsheng Li</em></p>
392
393      <p>International Conference on Learning Representations (<strong>ICLR</strong>) in 2023, <strong>Spotlight</strong></p>
394
395      <p><a href="https://shuguang-52.github.io/papers/23iclr_ea_has_bench.pdf">pdf</a>
396 /
397 <a href="http://shuguang-52.github.io/eahasbench">project page</a>
398 /
399 <a href="https://github.com/microsoft/EA-HAS-Bench">code (GitHub)</a>
400 /
401 <a href="https://openreview.net/forum?id=n-bvaLSCC78">Website Online</a></p>
402
403    </div>
404  </div>
405
406  <div class="col-sm-11 clearfix">
407    <div class="well">
408      <pubtit>Human Co-Parsing Guided Alignment for Occluded Person Re-identification</pubtit>
409
410      <p><img src="/images/pubpic/22_tip_hcga.png" class="img-responsive" width="200px" style="float: left" /></p>
411
412      <p>We propose a novel Human Co-parsing Guided Alignment (HCGA) framework that alternately trains the human co-parsing network and the ReID network, where the human co-paring network is trained in a <em>weakly supervised manner</em> to obtain paring results without any extra annotation.</p>
413
414      <p><em>S<strong>huguang Dou</strong>, Cairong Zhao, Xinyang Jiang, Shanshan Zhang, Member, IEEE, Wei-Shi Zheng, Wangmeng Zuo, Senior Member, IEEE</em></p>
415
416      <p>IEEE Transactions on Image Processing (<strong>T-IP</strong>) in 2022, CCF A</p>
417
418      <p><a href="https://shuguang-52.github.io/papers/22tip_hcga.pdf">pdf</a>
419 /
420 <a href="http://shuguang-52.github.io/hcga">project page</a>
421 /
422 <a href="https://github.com/Vill-Lab/2022-TIP-HCGA">code (GitHub)</a>
423 /
424 <a href="https://ieeexplore.ieee.org/abstract/document/9994734">Website Online</a></p>
425
426    </div>
427  </div>
428
429  <div class="col-sm-11 clearfix">
430    <div class="well">
431      <pubtit>Detecting Overlapped Objects in X-ray Security Imagery by a Label-aware Mechanism</pubtit>
432
433      <p><img src="/images/pubpic/22_tifs_clcxray.jpg" class="img-responsive" width="200px" style="float: left" /></p>
434
435      <p>One of the key challenges to the X-ray security checkis to detect the overlapped items in backpacks or suitcases in the X-ray images. Most existing methods improve the robustness of models to the object overlapping problem by enhancing the underlying visual information such as colors and edges. However, this strategy ignores the situations in that the objects have similar visual clues as to the background, and objects overlapping each other. Since the two cases rarely appear in existing datasets, we contribute a novel dataset – Cutters and Liquid Containers X-ray Dataset (CLCXray) to complete the related research.</p>
436
437      <p><em>Cairong Zhao*, Liang Zhu*, <strong>Shuguang Dou</strong>, Weihong Deng, and Liang Wang, Fellow, IEEE (*Co-First Authors)</em></p>
438
439      <p>IEEE Transactions on Information Forensics and Security (<strong>T-IFS</strong>) in 2022, CCF A</p>
440
441      <p><a href="https://shuguang-52.github.io/papers/22tifs_clcxray.pdf">pdf</a>
442 /
443 <a href="http://shuguang-52.github.io/clcxray">
443project page</a>
444 /
445 <a href="https://github.com/Vill-Lab/2022-TIFS-CLCXray">code (GitHub)</a>
446 /
447 <a href="https://ieeexplore.ieee.org/document/9722843">Website Online</a></p>
448
449    </div>
450  </div>
451
452  <div class="col-sm-11 clearfix">
453    <div class="well">
454      <pubtit>Incremental Generative Occlusion Adversarial Suppression Network for Person ReID</pubtit>
455
456      <p><img src="/images/pubpic/21_tip_igoas.png" class="img-responsive" width="200px" style="float: left" /></p>
457
458      <p>To address the occlusion problem, we propose a novel Incremental Generative Occlusion Adversarial Suppression (IGOAS) network.</p>
459
460      <p><em>Cairong Zhao*, Xinbi Lv*, <strong>Shuguang Dou*</strong>, Shanshan Zhang, Member, IEEE, Jun Wu, Senior Member, IEEE, and Liang Wang, Fellow, IEEE (*Co-First Authors)</em></p>
461
462      <p>Accepted by IEEE Transactions on Image Processing (<strong>T-IP</strong>) in 2022, CCF A</p>
463
464      <p><a href="https://shuguang-52.github.io/papers/21tip_igoas.pdf">pdf</a>
465 /
466 <a href="http://shuguang-52.github.io/igoas">project page</a>
467 /
468 <a href="https://github.com/Vill-Lab/2021-TIP-IGOAS">code (GitHub)</a>
469 /
470 <a href="https://ieeexplore.ieee.org/abstract/document/9397375">Website Online</a></p>
471
472    </div>
473  </div>
474
475  <div class="col-sm-11 clearfix">
476    <div class="well">
477      <pubtit>Alternately Updated Spectral–Spatial Convolution Network for the Classification of Hyperspectral Images</pubtit>
478
479      <p><img src="/images/pubpic/aussc_19_remote_sensing.png" class="img-responsive" width="200px" style="float: left" /></p>
480
481      <p>The connection structure in the convolutional layers of most deep learning-based algorithms used for the classification of hyperspectral images (HSIs) has typically been in the forward direction. In this study, an end-to-e
481nd alternately updated spectral–spatial convolutional network (AUSSC) with a recurrent feedback structure is used to learn refined spectral and spatial features for HSI classification.</p>
482
483      <p><em>Wenju Wang, <strong>Shuguang Dou^</strong>, Sen Wang (^Corresponding Author and First Student Author)</em></p>
484
485      <p>Remote sensing 2019</p>
486
487      <p><a href="https://shuguang-52.github.io/papers/19remotesensing_aussc.pdf">pdf</a>
488 /
489 <a href="http://shuguang-52.github.io/aussc">project page</a>
490 /
491 <a href="https://github.com/shuguang-52/AUSSC">code (GitHub)</a>
492 /
493 <a href="https://www.mdpi.com/2072-4292/11/15/1794">Website Online</a></p>
494
495    </div>
496  </div>
497
498  <div class="col-sm-11 clearfix">
499    <div class="well">
500      <pubtit>A fast dense spectral-spatial convolutional network framework for hyperspectral image classification</pubtit>
501
502      <p><img src="/images/pubpic/fdssc_18_remote_sensing.png" class="img-responsive" width="200px" style="float: left" /></p>
503
504      <p>To reduce the training time and improve accuracy, in this paper we propose an end-to-e
504nd fast dense spectral–spatial convolution (FDSSC) framework for HSI classification.</p>
505
506      <p><em>Wenju Wang, <strong>Shuguang Dou^</strong>, Zhongmin Jiang and Liujie Sun (^Corresponding Author and First Student Author)</em></p>
507
508      <p>Remote sensing 2018(<strong>ESI Highly Cited Paper Citation 300+</strong>)</p>
509
510      <p><a href="https://shuguang-52.github.io/papers/18remotesensing_fdssc.pdf">pdf</a>
511 /
512 <a href="http://shuguang-52.github.io/fdssc">project page</a>
513 /
514 <a href="https://github.com/shuguang-52/FDSSC">code (GitHub)</a>
515 /
516 <a href="https://www.mdpi.com/2072-4292/10/7/1068">Website Online</a></p>
517
518    </div>
519  </div>
520
521  <p><br clear="all" /></p>
522
523  <h4 id="see-all-publications"><a href="/publications">see all publications</a></h4>
524
525</div>
526
527<div class="col-sm-12">
528
529  <h3 id="friends">Friends</h3>
530
531  <p>Jiaqing Zhang (https://icey-zhang.github.io/) :
532<em>My girlfriend!</em></p>
533
534  <p>—————————————————————————————— :
535<em>This is a dividing line.</em></p>
536
537  <p>YuanPeng Tu (https://yuanpengtu.github.io/) :
538<em>The most abstract man in ViLL Lab.</em></p>
539
540  <p>Zifan Song (https://matcaviar.github.io/) :
541<em>The boy at Vill Lab gets a ten out of ten.</em></p>
542
543  <p>Shuyang Feng :
544<em>The most thoughtful man in ViLL Lab.</em></p>
545
546  <p>Junyao Gao (https://jeoyal.github.io/home/) :
547<em>A sincere person in Vill Lab.</em></p>
548
549  <p>Yubin Wang (https://thomaswangy.github.io/) :
550<em>The most trustworthy person in Vill lab.</em></p>
551
552  <p>Wenli Sun :
553<em>My makeup artist in Vill lab.</em></p>
554
555  <p><a href="/friends.html">see all friends</a></p>
556
557  <h3 id="theses">Theses</h3>
558
559  <div class="col-sm-11 clearfix">
560    <div class="well">
561      <pubtit>Research on Feature Learning and Application of Three-dimensional Point Cloud</pubtit>
562
563      <p><img src="/images/pubpic/3D.png" class="img-responsive" width="200px" style="float: left" /></p>
564
565      <p>Learning deep features from 3D point cloud for classification and retrieval. This work will be done before 12/2019.</p>
566
567      <p><em>Shuguang Dou</em></p>
568
569      <p>Master Thesis, College of Communication and Art Design, University of Shanghai for Science and Technology; December, 2019</p>
570
571      <p><a href="">pdf in Chinese (Temporarily unavailable)</a></p>
572
573    </div>
574  </div>
575
576  <p>   </p>
577
578</div>
579
580</div>
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