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font-family:Georgia, serif">important dates</a> 113 </li> 114 115 <li> 116 <a href="index.html#invited speakers" class="nav-link text-left" style="font-size:15px; font-family:Georgia, serif">invited speakers</a> 117 </li> 118 119 <!-- <li> 120 <a href="index.html#schedule" class="nav-link text-left" style="font-size:15px; font-family:'Times New Roman', Times, serif">schedule</a> 121 </li> --> 122 123 <li> 124 <a href="index.html#organizers" class="nav-link text-left" style="font-size:15px; font-family:Georgia, serif">organizers</a> 125 </li> 126 127 <!-- <li> 128 <a href="evaluation.html" class="nav-link text-left">Accepted papers</a> 129 </li> --> 130 131 <!-- <li> 132 <a href="leaderboard.html" class="nav-link text-left">leaderboard</a> 133 </li> --> 134 135 <!-- <li class="nav-item dropdown"> 136 <a class="nav-link dropdown-toggle" href="challenge.html" id="navbarDropdown" 137 role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false"> 138 Previous 139 </a> 140 <div class="dropdown-menu" aria-labelledby="navbarDropdown"> 141 <a class="dropdown-item" href="https://fllc-icpr2020.github.io/home/">FLLC2_ICPR2020</a> 142 <a class="dropdown-item" href="https://facial-landmarks-localization-challenge.github.io/#index">FLLC1_ICME2019</a> 143 </div> --> 144 </li> 145 </ul> 146 </nav> 147 148 </div> 149 150 </div> 151 </div> 152 153 </div> 154 155</div> 156 157<div class="site-blocks-cover overlay inner-page-cover" style="background-image: url('imgs/background.png');" 158 159 160 data-stellar-background-ratio="0.5"> 161 <div class="container"> 162 <div class="row align-items-center justify-content-center"> 163 <div class="col-md-10 text-center" data-aos="fade-up"> 164<!-- <h3> The 2nd Learning from Imperfect Data (LID) Workshop</h3>--> 165 <br> 166 <!-- style="font-size:40px" --> 167 <!-- 1<sup>st</sup> --> 168 <h1 style="font-size:46px">Human-centric Trustworthy Computer Vision</h1> 169 <h1 style="font-size:46px">From Research to Applications</h1> 170 171 <br><br><br> 172 <h4> In Conjunction with ICCV 2021</h4> 173 <h4> October 17 2021, Virtually</h4> 174 175 <!-- <br> --> 176 <!-- <a class="btn" href="https://wj.qq.com/s2/8104361/7743">
176Click to sign up</a> --> 177 178 </div> 179 </div> 180 </div> 181</div> 182 183 184 185<div class="site-section"> 186 <div class="container"> 187 188<!------------------------------ news ------------------------------------------> 189 190 191<div class="col-lg-12" id="news" style="padding-top:80px;margin-top:-150px;"> 192 <h4><i>News</i> !</h4> 193 194 <ul> 195 <li> 196 <p style="height: 10px"> 197 <strong style="font-size:20px;color:red;font-family:'Times New Roman';">July 23, 2021: </strong> We have postponed the submission deadline by one week. 198 </p> 199 </li> 200 201 <li> 202 <p style="height: 10px"> 203 <strong style="font-size:20px;color:red;font-family:'Times New Roman';">July 23, 2021: </strong> The top ranking papers will be recommended to ACM TOMM Special Issue. 204 </p> 205 </li> 206 <li> 207 <p style="height: 10px"> 208 <strong style="font-size:20px;color:red;font-family:'Times New Roman';">June 23, 2021:  </strong>The CMT submissions website: 209 <a href="https://cmt3.research.microsoft.com/HTCV2021/Submission/Index">https://cmt3.research.microsoft.com/HTCV2021</a> 210 </p> 211 </li> 212 213 <li> 214 <p style="height: 10px"> 215 <strong style="font-size:20px;color:red;font-family:'Times New Roman';">June 15, 2021:  </strong>The workshop date is presented. 216 </p> 217 </li> 218 <li> 219 <p style="height: 10px"> 220 <strong style="font-size:20px;color:red;font-family:'Times New Roman';"> May 10, 2021:   </strong>The website is coming. Call for papers. 221 </p> 222 <!-- <p style="height: 10px"> 223 <strong style="font-size:16px;color:red">Apr. 2, 2021:  </strong> 224 The grand challenge track is open. Click <a href="https://cmt3.research.microsoft.com/ICMEW2021/Track/10/Submission/Create">here</a> to submit your paper. 225 The deadline is April 7, 2021 [11:59 pm PST] 226 </p> 227 228 <p style="height: 10px"> 229 <strong style="font-size:16px;color:red">Apr. 1, 2021:  </strong> 230 The test phase begins. Please submit the model and paper following the <a href="evaluation.html#finalSubmission">submission guideline</a>. 231 </p> 232 233 <p style="height: 10px"> 234 <strong style="font-size:16px;color:red">Mar. 4, 2021:  </strong> 235 The 3rd Grand Challenge of 106-Point Facial Landmark Localization is opening now. Welcome to participate! 236 </p> --> 237 238 </li> 239 </ul> 240</div> 241<br><br><br><br> 242 243<!------------------------ overview -----------------------> 244 <div class="col-lg-12"> 245 <div class="section-title"> 246 <h2>Overview</h2> 247 <br> 248 <h4 style="font-size: 21px;"><i>How to define, pursue and evaluate trustworthy technologies for human-centric computer vision tasks?</i></h4> 249 </div> 250 <div class="trend-entry d-flex"> 251 <div class="trend-contents"> 252 <p> 253 254    With the rapid technical progress in computer vision and the spread of vision-based applications over the past several years, the human-centric computer vision technologies, such as person re-identification, face recognition, action recognition, <i>etc</i>., are quickly becoming an essential enabler for many fields. Although, it brings great value to individuals and society, it is also encounters a variety of novel ethical, legal, social, and security challenges. Particularly, in recent years, the multiple multimedia sensing technologies as well as the large-scale computing and storage infrastru
254ctures are stimulating at a rapid velocity a wide variety of human-centric big data, which provides rich knowledge to help the development of these applications. Meanwhile, such data contains a large amount of personal private information, bringing concerns about the safety and trustworthiness of computer vision technologies. Consequently, trustworthy computer vision has been attracting an increasing attention from academia and industry. It focuses on human-oriented, fair, robust, interpretable, and responsible vision technologies, and is also at the core of the next-generation artificial intelligence (AI). The goal of this workshop is to: <font face="Times New Roman">1</font>) bring together the state-of-the-art research on human-centric vision analysis for trustworthy AI; <font face="Times New Roman">2</font>) call for a coordinated effort to understand the opportunities and challenges emerging in human-centric trustworthy vision technologies; <font face="Times New Roman">3</font>) explore the fairness, robustness, interpretability and accountability oriented to human; <font face="Times New Roman">4</font>) showcase innovative methodologies and ideas; <font face="Times New Roman">5</font>) introduce interesting real-world human-oriented trustworthy systems and applications; <font face="Times New Roman">6</font>) give insight into industryâs practice of trustworthy AI for human-centric vision and discuss future directions. We solicit original contributions in all fields of trustworthy human analysis to help us better understand the nature of vision algorithms for real-world applications. We hope the workshop offer a timely collection of research updates to benefit the researchers and practitioners working in the broad computer vision, pattern recognition, and trustworthy AI communities. 255 <br> 256 </p> 257 </div> 258 </div> 259 </div> 260<!------------------------ Call for papers ---------------------> 261<div class="col-lg-12" id="call for papers" style="padding-top:80px;margin-top:-80px;"> 262 <div class="section-title"> 263 264 <br><br><br> 265 <h2>Call for papers</h2> 266 </div> 267 268 <div class="trend-entry d-flex"> 269 <div class="trend-contents"> 270 <p style="margin: auto;"> 271    We invite submissions for ICCV <font face='Times New Roman'>2021</font> Workshop, Human-centric Trustworthy Computer Vision: From Research to Applications (HTCV<font face="Times New Roman">2021</font>), that brings researchers together to discuss human-oriented, fair, robust, interpretable, and responsible technologies for human-centric vision analysis. We solicit original research and survey papers from <font face="Times New Roman" size=5px>5</font> to <font face="Times New Roman" size=5px>8</font> pages (excluding references and appendices). Each submitted paper will be double-blind peer reviewed by at least three reviewers. All accepted papers will be presented as either oral or poster presentations, with a best paper award, and appear in the <a href="https://openaccess.thecvf.com/menu" style="font-family: 'Times New Roman', Times, serif; font-size:21px;">CVF open access archive.</a> 272 Papers submission is through <a href="https://cmt3.research.microsoft.com/HTCV2021/Submission/Index" style="font-family: 'Times New Roman', Times, serif; font-size:21px;">HTCV<font face="Times New Roman">2021</font> CMT</a> and must follow the same policies and submission guidelines described in <a href="http://iccv2021.thecvf.com/node/4#submission-guidelines" style="font-family: 'Times New Roman', Times, serif; font-size:21px;">ICCV'<font face="Times New Roman">21</font> Author Guidelines</a>. Papers that violates the anonymity, do not use the ICCV submission template or have more than <font face="Times New Roman">8</font> pages (excluding references and appendices) will be rejected without review. In submitting a manuscript to this workshop, the authors acknowledge that no paper substantially similar in content has been submitted to another workshop or conference during the review period. 273 <br> 274   The scope of this workshop includes, but is not limited to, the following topics: 275 </p> 276 <ul class="set_ul" style="margin-bottom: 0px; margin-left:20px; display: inline-block;"> 277 <li> 278 Adversarial attack and defense in face recognition and person re-identification 279 </li> 280 <li> 281 Explainable face and body analysis, generation and edition 282 </li> 283 <li> 284 Robust human body and face representation learning 285 </li> 286 <li> 287 Face anti-spoofing and deep-fake detection 288 </li> 289 <li> 290 Robust gait and action recognition 291 </li> 292 <li> 293 Secured federated learning 294 </li> 295 <li> 296 Robustness against evolving attacks in computer vision 297 </li> 298 <li> 299 Fairness analysis for data and models of face or human recognition 300 </li> 301 <li> 302 Trustworthy algorithms, frameworks, and tools for Human-centric Trustworthy Computer Vision 303 </li> 304 </ul> 305 <p> 306   The top ranking papers will be recommended to ACM TOMM Special Issue. 307 </p> 308 </div> 309 </div> 310 </div> 311 312 313<!------------------------ import dates ---------------------> 314 315<div class="col-lg-12" id="dates" style="padding-top:80px;margin-top:-80px;"> 316 <div class="section-title"> 317 318 <br><br><br> 319 <h2>Important Dates</h2> 320 </div> 321 <div class="trend-entry d-flex"> 322 <table class="table table-striped" style="border-bottom:2px solid #C4C4C4;border-top:2px solid #C4C4C4;width:60%; line-height: 12px;" align="center"> 323 <thead> 324 <!-- <tr style="background-color:#BFEFFF;"> --> 325 <tr> 326 <th scope="col" style="text-align: center;"> Description</th> 327 <th scope="col" style="text-align: center;"> Date (Pacific Time)</th> 328 </tr> 329 </thead> 330 <tbody> 331 <!-- <tr style="background-color:#8BB1D8;"> --> 332 <tr style="background-color:#B6CEE7;"> 333 <td style="color: rgb(230, 33, 33); font-weight: bold;">Submission Deadline</td> 334 <td style="color: rgb(230, 33, 33); font-weight: bold;">August 8, 2021 (11:59PM)</td> 335 </tr> 336 337 <tr> 338 <td>Decisions to Authors</td> 339 <td> August 14, 2021 (11:59PM)</td> 340 </tr> 341 342 <!-- <tr style="background-color:#8BB1D8;"> --> 343 <tr style="background-color:#B6CEE7;"> 344 <td>Camera-ready Due</td> 345 <td> August 18, 2021 (11:59PM)</td> 346 </tr> 347 348 <tr> 349 <td>Workshop Date</td> 350 <td> October 17, 2021 (Afternoon)</td> 351 </tr> 352 353 </tbody> 354 355 </table> 356 </div> 357 358 359 </div> 360 361<!------------------------ Agenda ---------------------> 362 363<div class="col-lg-12" id="dates" style="padding-top:80px;margin-top:-80px;"> 364 <div class="section-title"> 365 <br><br><br> 366 <h2>Agenda</h2> 367 </div> 368 <div class="trend-entry d-flex"> 369 370 <table class="table table-striped" style="border-bottom:2px solid #C4C4C4;border-top:2px solid #C4C4C4;width:100%; line-height: 20px;" align="center"> 371 <!-- <table class="table table-striped" style="border-bottom:2px solid #C4C4C4;border-top:2px solid #C4C4C4;width:100%; line-height: 20px;" text-align="left"> --> 372 <thead> 373 <tr> 374 <th scope="col" style="text-align: left;">  Time (EDT)</th> 375 <th scope="col" style="text-align: left;"> Session</th> 376 <th scope="col" style="text-align: left;"> Speaker</th> 377 </tr> 378 </thead> 379 <tbody> 380 <!-- <tr style="background-color:#8BB1D8;"> --> 381 <tr style="background-color:#B6CEE7;"> 382 <td>1:00 PM-1:10 PM</td> 383 <td style="text-align: left;">Opening</td> 384 <td style="text-align: left;">Host</td> 385 </tr> 386 387 <tr> 388 <td>1:10 PM-1:55 PM</td> 389 <td style="text-align: left;">Invited speak 1</td> 390 <td style="text-align: left;">"Physical and digital fake face detection" Zhen Lei</td> 391 </tr> 392 393 <tr style="background-color:#B6CEE7;"> 394 <td>1:55 PM-2:40 PM</td> 395 <td style="text-align: left;">Invited speak 2</td> 396 <td style="text-align: left;">"Probabilistic Modeling for Human Mesh Recovery" Georgios Pavlakos</td> 397 </tr> 398 399 <tr> 400 <td>2:40 PM-3:25 PM</td> 401 <td style="text-align: left;">Invited speak 3</td> 402 <td style="text-align: left;">"Pitfalls on the Road to Trust in Computer Vision" Karthik Nandakumar</td> 403 </tr> 404 405 <!-- <tr style="background-color:#8BB1D8;"> --> 406 <tr style="background-color:#B6CEE7;"> 407 <td>3:25 PM-4:10 PM</td> 408 <td style="text-align: left;">Invited speak 4</td> 409 <td style="text-align: left;">"Challenges of Creating Affective Computational Tools for<br>Behavioral and Clinical Sciences" Albert Ali Salah</td> 410 </tr> 411 412 <tr> 413 <td>4:10 PM-4:25 PM</td> 414 <td style="text-align: left;">Oral presentation 1</td> 415 <td style="text-align: left;">âMulti-Perspective Features Learning for Face Anti-Spoofingâ</td> 416 </tr> 417 418 <tr style="background-color:#B6CEE7;"> 419 <td>4:25 PM-4:40 PM</td> 420 <td style="text-align: left;">Oral presentation 2</td> 421 <td style="text-align: left;">âRethinking Common Assumptions to Mitigate Racial Bias in<br>Face Recognition Datasetsâ</td> 422 </tr> 423 424 <tr> 425 <td>4:40 PM-4:55 PM</td> 426 <td style="text-align: left;">Oral presentation 3</td> 427 <td style="text-align: left;">âTransformer Meets Part Model: Adaptive Part Division for<br>Person Re-Identificationâ</td> 428 </tr> 429 430 <!-- <tr style="background-color:#8BB1D8;"> --> 431 <tr style="background-color:#B6CEE7;"> 432 <td>4:55 PM-5:10 PM</td> 433 <td style="text-align: left;">Oral presentation 4</td> 434 <td style="text-align: left;">âSVEA: A Small-scale Benchmark for Validating the Usability of<br>Post-hoc Explainable AI Solutions in Image and Signal Recognitionâ</td> 435 </tr> 436 437 <tr> 438 <td>5:10 PM-5:20 PM</td> 439 <td style="text-align: left;">Best Paper Announcement</td> 440 <td style="text-align: left;">Host</td> 441 </tr> 442 443 <tr style="background-color:#B6CEE7;"> 444 <td>5:20 PM-5:55 PM</td> 445 <td style="text-align: left;">Poster presentation</td> 446 <td style="text-align: left;">Pre-recorded videos</td> 447 </tr> 448 449 <tr> 450 <td>5:55 PM-6:00 PM</td> 451 <td style="text-align: left;">Ending</td> 452 <td style="text-align: left;">Host</td> 453 </tr> 454 455 </tbody> 456 457 </table> 458 </div> 459 460 461 </div> 462 463 464 465 466 467<!------------------------------ Invited speakers ------------------------------------------> 468 469<div class="col-lg-12" id="invited speakers" style="padding-top:80px;margin-top:-80px;"> 470 <div class="section-title"> 471 472 <br><br><br> 473 <h2>Invited speakers</h2> 474 <br><br> 475 476 <div align="left"> 477 478 479 <div class="instructor_fina" style="float: left; display: inline;
479 margin-top: 1px;"> 480 <a href="http://www.cbsr.ia.ac.cn/users/zlei/"> 481 <div class="instructorphoto"><img src="imgs/ZhenLei.png"></div> 482 </a> 483 <div style="font-family:Helvetica, sans-serif; font-size: 17px;display: inline-block;">Prof.</div> 484 <a href="http://www.cbsr.ia.ac.cn/users/zlei/"> 485 <div style="font-size: 17px;display: inline-block;"> Zhen Lei</div> 486 </a> 487 <div style="font-family:Helvetica, sans-serif; font-size: 17px">NLPR, CASIA, China</div> 488 </div> 489 490 <div style="font-family:Times New Roman, sans-serif; font-size: 20px; display: inline-block; width: 78%;text-align: justify;"> 491 <strong>Title:</strong> Physical and digital fake face detection 492 <br> 493 <strong>Abstract:</strong> Physical and digital face attack are two common face attacks in reality. In many applications, we have to detect these two attacks to assure the security of the system. Physical fake face detection, also known as presentation attack detection or face anti-spoofing, is used to judge the genuine/fake face in front of a camera. The digital fake face detection is used to detect face forgery in the internet. In the first part, I will report recent progress on how to learn the optimal supervision signal so that the model can be better learned. In the second part, Iâll introduce a 3D decomposition based digital fake face detection method and show its effectiveness in face forgery detection task. 494 </div> 495 496 <br> 497 <br> 498 <br> 499 <br> 500 501 <!-- <div class="instructor_fina" style="float: left; display: inline;margin-top: 100px;"> --> 502 <div class="instructor_fina" style="float: left; display: inline; margin-top: 4px;"> 503 504 <a href="https://geopavlakos.github.io/"> 505 <div class="instructorphoto"><img src="imgs/Georgios Pavlakos.png"></div> 506 </a> 507 <div style="font-family:Helvetica, sans-serif; font-size: 17px; display: inline-block;">Dr.</div> 508 <a href="https://geopavlakos.github.io/"> 509 <div style="font-size: 16px; display: inline-block;"> Georgios Pavlakos</div> 510 </a> 511 <div style="font-family:Helvetica, sans-serif;font-size: 17px">UC Berkeley, USA</div> 512 </div> 513 514 515 <div style="font-family:Times New Roman, sans-serif; font-size: 20px; display: inline-block; width: 78%;text-align: justify;"> 516 <strong>Title:</strong> Probabilistic Modeling for Human Mesh Recovery 517 <br> 518 <strong>Abstract:</strong> Reconstructing humans in 3D from a single image is an inherently ambiguous problem since multiple 3D poses can lead to the same reprojection. However, most related works only return one pose estimate for each input image. This fails to capture the multimodal aspect of the problem and results in systems with potentially non-interpretable or non-trustworthy behavior. In this talk, I will discuss our recent work that tries to embrace this multimodality and recasts the problem as learning a mapping from the input to a distribution of plausible 3D poses. Our approach is based on the normalizing flows model and offers a series of advantages. For conventional applications, where a single 3D estimate is required, our formulation allows for efficient mode computation. Using the mode leads to performance that is comparable with the state of the art among deterministic unimodal regression models. Simultaneously, we demonstrate that our model is useful in a series of downstream tasks, where we leverage the probabilistic nature of the prediction as a tool for more accurate estimation. These tasks include reconstruction from multiple uncalibrated views, as well as human model fitting, where our model acts as a powerful image-based prior for mesh recovery. 519 </div> 520 521 522 <br> 523 <br> 524 <br> 525 <br> 526 527<!-- <div class="instructor_fina" style="float: left; display: inline;margin-top:100px;"> --> 528 <div class="instructor_fina" style="float: left; display: inline;margin-top: -10px;"> 529 <a href="https://www.sprintai.org/nkarthik"> 530 <div class="instructorphoto"><img src="imgs/KarthikNandakumar.png"></div> 531 </a> 532 <div style="font-family:Helvetica, sans-serif; font-size: 17px; display: inline-block;">Prof.</div> 533 <a href="https://www.sprintai.org/nkarthik"> 534 <div style="font-size: 16px; display: inline-block;"> Karthik Nandakumar</div> 535 </a> 536 <div style="font-family:Helvetica, sans-serif;font-size: 17px">MUZUAI, Abu Dhabi</div> 537 </div> 538 <div style="font-family:Times New Roman, sans-serif; font-size: 20px; display: inline-block; width: 78%;text-align: justify;"> 539 <strong>Title:</strong> Pitfalls on the Road to Trust in Computer Vision 540 <br> 541 <strong>Abstract:</strong>
541 Computer vision systems have had a profound positive impact on human lives in applications ranging from smartphone access control to self-driving vehicles and automated medical image diagnosis. While an exponential increase in data availability, combined with algorithmic advancements in machine learning such as deep neural network models and rapid improvements in computational capabilities have powered the growth in computer vision over the past two decades, the field is at a crossroad now due to a lack of trust among human users. Numerous concerns over the trustworthiness of computer vision systems have been raised by various stakeholders and these include: (i) safety and robustness of computer vision algorithms against adversarial attacks, (ii) potential for causing discriminatory harm against specific population groups, (iii) breach of data privacy and confidentiality, (iv) inability to provide explainable decisions, and (v) overall lack of accountability and auditability. In this talk, we will first review the above pitfalls on the road to trust and the interactions among them. Next, we discuss solutions that have been proposed in the literature to address these concerns. Finally, we identify possible research directions that can facilitate the safe navigation of these pitfalls and take us to the eventual destination of developing computer vision systems that can be trusted by human users. 542 </div> 543 544 545 <br> 546 <br> 547 <br> 548 <br> 549 550 <!-- <div class="instructor_fina" style="float: left; display: inline;margin-top:40px"> --> 551 <div class="instructor_fina" style="float: left; display: inline;margin-top: 7px;"> 552 553 <a href="https://webspace.science.uu.nl/~salah006/"> 554 <div class="instructorphoto"><img src="imgs/AlbertAliSalah.png"></div> 555 </a> 556 <div style="font-family:Helvetica, sans-serif; font-size: 17px;display: inline-block;">Prof.</div> 557 <a href="https://webspace.science.uu.nl/~salah006/"> 558 <div style="font-size: 17px;display: inline-block;"> Albert Ali Salah</div> 559 </a> 560 <div style="font-family:Helvetica, sans-serif; font-size: 17px">Utrecht University, Netherlands</div> 561 </div> 562 563 <div style="font-family:Times New Roman, sans-serif; font-size: 20px; display: inline-block; width: 78%;text-align: justify;"> 564 <strong>Title:</strong> Challenges of Creating Affective Computational Tools for Behavioral and Clinical Sciences 565 <br> 566 <strong>Abstract:</strong> Automatic analysis of human affective and social signals brought computer science closer to social sciences and, in particular, enabled collaborations between computer scientists and behavioral scientists. 567 In this talk, I highlight the main research areas in this burgeoning interdisciplinary area, and provide an overview of the opportunities and challenges. Drawing on examples from our recent research, such as automatic analysis of interactive play therapy sessions with children, and diagnosis of bipolar disorder from multimodal cues, as well as relying on recent examples from the growing literature, I will explore the potential of human-AI collaboration, where AI systems do not replace, but support monitoring and human decision making in behavioral and clinical sciences. I conclude with some controversial issues that may point out what "trustworthy AI" would mean for the technologies developed in this domain. 568 569 </div> 570 571 572 573 </div> 574 575</div> 576 577</div> 578 579 580 581 582<!---------------------------- organizers ----------------------------------------> 583<div class="col-lg-12" id="organizers" style="padding-top:80px;margin-top:-80px;"> 584 585 <!---------------------------- organizer ----------------------------------------> 586 <div class="section-title"> 587 <br><br><br> 588 <h2>organizers</h2> 589 </div> 590 591 <div align="center"> 592 <div class="instructor_mine"> 593 <a href="http://www.cs.ucf.edu/~liujg/"> 594 <div class="instructorphoto"><img src="imgs/JinggenLiu.png"></div> 595 <div style="font-size: 18px">Jingen Liu</div> 596 </a> 597 <div style="font-family:Helvetica, sans-serif;font-size: 18px">JD AI Research, USA 598 </div> 599 </div> 600 601 <div class="instructor_mine"> 602 <a href="https://www.sifeiliu.net/"> 603 <div class="instructorphoto"><img src="imgs/SifeiLiu.png"></div> 604 <div style="font-size: 18px">Sifei Liu</div> 605 </a> 606 <div style="font-family:Helvetica, sans-serif;font-size: 18px">Nvidia Research, USA</div> 607 </div> 608 609 <div class="instructor_mine"> 610 <a href="http://drliuwu.com/english.html"> 611 <div class="instructorphoto"><img src="imgs/WuLiu.png"></div> 612 <div style="font-size: 18px">Wu Liu</div> 613 </a> 614 <div style="font-family:Helvetica, sans-serif;font-size: 18px">JD AI Research, China</div> 615 </div> 616 617 <div class="instructor_mine"> 618 <a href="http://disi.unitn.it/~sebe/"> 619 <div class="instructorphoto"><img src="imgs/NicuSebe.png"></div> 620 <div style="font-size: 18px">Nicu Sebe</div> 621 </a> 622 <div style="font-family:Helvetica, sans-serif;font-size: 18px">UniTN, Italy</div> 623 </div> 624 625 <div class="instructor_mine"> 626 <a href="https://sites.google.com/view/hailin-shi"> 627 <div class="instructorphoto"><img src="imgs/HailinShi.png"></div> 628 <div style="font-size: 18px">Hailin Shi</div> 629 </a> 630 <div style="font-family:Helvetica, sans-serif;font-size: 18px">JD AI Research, China</div> 631 </div> 632 </div> 633 634 <!---------------------------- Committe Chairs ----------------------------------------> 635 <div class="section-title"> 636 <br><br><br> 637 <h2>Committee Chairs</h2> 638 </div> 639 640 <div align="center"> 641 642 <div class="instructor"> 643 <a href="https://scholar.google.com/citations?user=vhM_c14AAAAJ&hl=zh-CN"> 644 <div class="instructorphoto"> 645 <img src="imgs/QianBao.png"> 646 </div> 647 <div style="font-size: 18px">Qian Bao</div> 648 </a> 649 <div style="font-family:Helvetica, sans-serif;font-size: 18px">JD AI Research, China</div> 650 </div> 651 652 <div class="instructor"> 653 <a href="https://aberhu.github.io/"> 654 <div class="instructorphoto"> 655 <img src="imgs/YiboHu.png"> 656 </div> 657 <div style="font-size: 18px">Yibo Hu</div> 658 </a> 659 <div style="font-family:Helvetica, sans-serif;font-size: 18px">JD AI Research, China</div> 660 </div> 661 662 <div class="instructor" style="width:240px;"> 663 <a href="https://www.aminer.cn/profile/junbo-guo/53f4652cdabfaeecd6a08551"> 664 <div class="instructorphoto"> 665 <img src="imgs/guojunbo.png"> 666 </div> 667 <div style="font-size: 18px">Junbo Guo</div> 668 </a> 669 <div style="font-family:Helvetica, sans-serif;font-size: 18px;">People's Daily Online, China</div> 670 </div> 671 672 673 </div> 674 </div> 675 <!----------------------------PC members----------------------------------------> 676 677 <div class="section-title"> 678 <br><br><br> 679      680 <h2>PC members</h2> 681                    682 <h2>Advisory board</h2> 683 </div> 684 685 686 <div class="set_ul" style="font-family: Georgia, serif, Baskerville, monospace; font-size: 20px; margin-left: 180px; width: 35%; display: inline-block;"> 687 <ul> 688 <li> 689 <a href="https://research-repository.uwa.edu.au/e
689n/persons/naveed-akhtar">Naveed Akhtar</a> (<font face=Helvetica>UWA, Australia)</font> 690 <!-- <a href="https://research-repository.uwa.edu.au/en/persons/naveed-akhtar">Naveed Akhtar</a> --> 691 </li> 692 693 <li> 694 <a href="https://sites.google.com/view/drjinghuang">Jing Huang</a> (<font face=Helvetica>JD AI Research, USA</font>) 695 </li> 696 697 <li> 698 <div style="font-family: Helvetica;">Haoran Jiang (SHU, China)</div> 699 <!-- Can Yang --> 700 </li> 701 702 <li> 703 <a href="https://sites.google.com/site/pingliu264/">Ping Liu</a> (<font face=Helvetica>A*STAR, Singapore</font>) 704 <!-- <a href="https://sites.google.com/site/pingliu264/">Ping Liu</a> --> 705 706 </li> 707 708 <li> 709 <a href="http://prclibo.github.io/">Bo Li</a> (<font face=Helvetica>JD AI Research, China</font>) 710 <!-- <a href="http://prclibo.github.io/">Bo Li</a> --> 711 </li> 712 713 <li> 714 <!-- <a href="https://www.facebook.com/public/Zheng-Sou/">Zheng Sou</a> (Facebook AI, USA) --> 715 <div style="font-family: Helvetica;">Zheng Sou (Facebook AI, USA)</div> 716 717 </li> 718 719 <li> 720 <a href="http://wuziyan.com/">Ziyan Wu</a> (<font face=Helvetica>UII, USA</font>) 721 <!-- <a href="http://wuziyan.com/">Ziyan Wu</a> --> 722 </li> 723 724 <li> 725 <a href="https://www.yf.io/">Fisher Yu</a> (<font face=Helvetica>ETH Zürich, Switzerland</font>) 726 <!-- <a href="https://www.yf.io/">Fisher Yu</a> --> 727 </li> 728 729 730 <li> 731 <div style="font-family: Helvetica;">Can Yang (JD AI Research, China)</div> 732 <!-- Can Yang --> 733 </li> 734 735 <li> 736 <a href="https://scie.shu.edu.cn/Prof/zengdan.htm">Dan Zeng</a> (<font face=Helvetica>SHU, China</font>) 737 <!-- <a href="https://sites.google.com/site/pingliu264/">Ping Liu</a> --> 738 </li> 739 740 </ul> 741 </div> 742 743 <div class="set_ul" style="font-family: Georgia, serif, Baskerville, monospace; font-size: 20px;margin-left: -20px; width: 40%; display:block; float: right;"> 744 <ul> 745 <li> 746 <a href="https://ps.is.mpg.de/~black">Michael Black</a> (<font face=Helvetica>MPI-IS, Germany</font>) 747 </li> 748 749 <li> 750 <a href="https://www.cs.umd.edu/people/lsdavis">Larry Davis</a> (<font face=Helvetica>UM, USA</font>) 751 </li> 752 753 <li> 754 <a href="https://www.cse.msu.edu/~liuxm/index2.html">Xiaoming Liu</a> (<font face=Helvetica>MSU, USA</font>) 755 </li> 756 757 <li> 758 <a href="https://taomei.me/">Tao Mei</a> (<font face=Helvetica>JD AI Research, China</font>) 759 </li> 760 761 <li> 762 <a href="http://www.cs.cmu.edu/~yaser/">Yaser Sheikh</a> (<font face=Helvetica>CMU, USA</font>) 763 </li> 764 </ul> 765 766 767 768<!-- 769 <div class="section-title"> 770 <br><br><br> 771 <h2>Advisors</h2> 772 </div> 773 774 <ul class="set_ul" style="margin-left: 300px;"> 775 <li> 776 <div style="font-family: Helvetica;">Larry Davis (UM, USA)</div> 777 </li> 778 779 <li> 780 <div style="font-family: Helvetica;">Micheal Black (MPI-IS, Germany)</div> 781 </li> 782 783 <li> 784 <div style="font-family: Helvetica;">Yaser Sheikh (CMU, USA)</div> 785 </li> 786 787 <li> 788 <div style="font-family: Helvetica;">Xiaoming Liu (MSU, USA)</div> 789 </li> 790 791 <li> 792 <div style="font-family: Helvetica;">Tao Mei (JD AI Research, China)</div> 793 </li> 794 </ul> --> 795 796 <!-- <div align="center"> 797 798 <div class="instructor"> 799 <a href="https://research-repository.uwa.edu.au/e
799n/persons/naveed-akhtar"> 800 <div class="instructorphoto"> 801 <img src="imgs/NaveedAkhtar.png"> 802 </div> 803 <div>Naveed Akhtar</div> 804 </a> 805 <div>University of Western Australia, Australia</div> 806 </div> 807 808 <div class="instructor"> 809 <a href="https://sites.google.com/site/pingliu264/"> 810 <div class="instructorphoto"> 811 <img src="imgs/PingLiu.png"> 812 </div> 813 <div>Ping Liu</div> 814 </a> 815 <div>A*STAR, Singapore</div> 816 </div> 817 818 <div class="instructor"> 819 <a href="https://www.facebook.com/public/Zheng-Sou/"> 820 <div class="instructorphoto"> 821 <img src="imgs/YiboHu.png"> 822 </div> 823 <div>Zheng Sou</div> 824 </a> 825 <div>Facebook AI, USA</div> 826 </div> 827 828 <div class="instructor"> 829 <a href="http://wuziyan.com/"> 830 <div class="instructorphoto"> 831 <img src="imgs/ZiyanWu.png"> 832 </div> 833 <div>Ziyan Wu</div> 834 </a> 835 <div>United Imaging Intelligence, USA</div> 836 </div> 837 838 <div class="instructor"> 839 <a href="https://www.yf.io/"> 840 <div class="instructorphoto"> 841 <img src="imgs/FisherYu.png"> 842 </div> 843 <div>Fisher Yu</div> 844 </a> 845 <div>ETH Z¨urich, Switzerland</div> 846 </div> 847 848 </div> --> 849 </div> 850 851 852 853<!------------------------------email--------------------------------> 854<br><br> 855 <p style="margin-top:30px;margin-bottom: 60px; text-align: center; font-family: 'Times New Roman', Times, serif; font-size: 24px;">If you have any questions, feel free to contact < [email protected] > </p> 856 857<!------------------------------ sponsor ------------------------------------------> 858<div class="section-title"> 859 <br> 860 <!-- <img src="images/logo.png" class="icon-align-center"> --> 861 <img src="imgs/JD AI Research.png" class="icon-align-center" style="margin-right: 60px;"> 862 <img src="imgs/Nvidia-logo.jpg" class="icon-align-center" style="margin-right: 60px;"> 863 <img src="imgs/University of Trento.png" class="icon-align-center"style="margin-right: 40px;"> 864 <img src="imgs/People's Daily Online.png" class="icon-align-center"> 865 866 867</div> 868<p style="margin-top:5px;margin-bottom: -30px; text-align: center; font-family: 'Times New Roman', Times, serif; font-size: 19px;">The workshop is organized in collaboration with JD AI Research, NVIDIA Research, University of Trento <br>and State Key Laboratory of Communication Content Cognition, People's Daily Online. </p> 869<!------------------------------- boarder ----------------------------------------> 870 <div class="col-lg-12"> 871 <div style="display:inline-block;width:500px;"> 872
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