PageSourceSearch

https://haoqifan.github.io/

html haoqifan.github.io collected 2026-10-03 09:16:38 UTC 42,497 bytes, 687 lines download raw bytes

1<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
2<html xmlns="http://www.w3.org/1999/xhtml">
3    <head>
4          <meta charset="UTF-8">
5          <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
6          <meta http-equiv="x-ua-compatible" content="ie=edge">
7
8        <meta http-equiv="content-type" content="text/html; charset=utf-8" />
9        <meta name="description" content="" />
10        <meta name="keywords" content="" />
11        <title>Haoqi Fan</title>
12        <link rel="stylesheet" type="text/css" href="css/style.css" />
13
14
15          <link rel="stylesheet" href="css/bootstrap.min.css">
16          <link rel="stylesheet" href="css/academicons.min.css">
17          <link rel="stylesheet" href="https://cdn.rawgit.com/jpswalsh/academicons/master/css/academicons.min.css">
18          <link rel="stylesheet" href="css/font-awesome.min.css">
19
20
21    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.11.2/css/all.min.css" integrity="sha256-+N4/V/SbAFiW1MPBCXnfnP9QSN3+Keu+NlB+0ev/YKQ=" crossorigin="anonymous">
22    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/fancybox/3.5.7/jquery.fancybox.min.css" integrity="sha256-Vzbj7sDDS/woiFS3uNKo8eIuni59rjyNGtXfstRzStA=" crossorigin="anonymous">
23    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.15.10/styles/dracula.min.css" crossorigin="anonymous" title="hl-dark" disabled>
24    
24<script src="https://cdnjs.cloudflare.com/ajax/libs/lazysizes/5.1.2/lazysizes.min.js" integrity="sha256-Md1qLToewPeKjfAHU1zyPwOutccPAm5tahnaw7Osw0A=" crossorigin="anonymous" async></script>
24
25    <link rel="stylesheet" href="https://fonts.googleapis.com/css?family=Libre+Baskerville%7CMontserrat:400,700%7CRoboto%7CRoboto Mono&display=swap">
26    <link rel="stylesheet" href="/css/academic.css">
27    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/highlight.js/9.15.10/styles/github.min.css" crossorigin="anonymous" title="hl-light">
28    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/academicons/1.8.6/css/academicons.min.css" integrity="sha256-uFVgMKfistnJAfoCUQigIl+JfUaP47GrRKjf6CTPVmw=" crossorigin="anonymous">
29
30            <style>
31            a.plain-black {
32              text-decoration: none;
33              color: black;
34              margin: 5px;
35            }
36          </style>
37    </head>
38    <body>
39    
40
41
42        <div id="wrapper">
43
44            <div id="inner">
45                <div id="page">
46                    <div id="content">
47                            <table>
48                            <tr>
49                                <td align="left" width = "180"><img class="image" src="image/haoqi_fan.jpg" width = "135px" alt="" /></td>                                <td>
50                                <h2>Haoqi Fan</h2>
51                                <p> Researcher in Computer Vision <br>
52                                    <a href="https://ai.facebook.com/" target="_blank">Facebook AI Research</a> <br>
53                                    E-mail: haoqifan [at] gmail [dot] com <br>
54                                    <a class="plain-black" href="mailto:[email protected]"><i class="fa fa-2x fa-envelope"></i></a>
55									<a class="plain-black" href="https://scholar.google.com/citations?user=76B8lrgAAAAJ&hl=en" target="_blank"><i class="ai ai-google-scholar-square ai-2x"></i></a>
56                                 </p>
57                                 </td>
58                                <td>
59                            </tr>
60                            </table>
61                            <br/>
62                            <h3>About Me</h3>
63                            <table>
64                            <tr>
65                                <td align="left" bgcolor="" width = "1000px",  line-height = "120px" >
66                                <p>Haoqi Fan is an AI Researcher who works on multimodal foundational models. He spent 7 years at <a href="https://ai.facebook.com/" target="_blank">Facebook AI Research (FAIR)</a>. He graduated from <a href = "https://www.ri.cmu.edu/" target="_blank">the Robotics Institute</a> of Carnegie Mellon University. His research interests lie in computer vision and deep learning.
67                                </p>
68                                </td>
69                              </tr>
70                            </table>
71
72                            <h3>Recent News</h3>
73
74
75                            <! –– BAGEL ––>
76                            <table>
77                            <tr>
78                                <td align="left" bgcolor="" width = "1000px" >
79                                    <li> <b>May 2025</b>: Released <a href="https://bagel-ai.org/">BAGEL</a> for unified multimodal research and application, it quickly soared to the #1 trending model on Hugging Face.&nbsp;<a href="https://github.com/bytedance-seed/BAGEL"><img alt="GitHub stars" src="https://img.shields.io/github/stars/bytedance-seed/BAGEL?style=social"></a></li>
80                                    <br/>
81                                </td>
82                              </tr>
83                            </table>
84
85
86
87                            <! –– PTV ––>
88                            <table>
89                            <tr>
90                                <td align="left" bgcolor="" width = "1000px" >
91                                    <li> <b>Apr 2021</b>: Released <a href = "https://pytorchvideo.org/" target="_blank">PyTorchVideo</a> - a deep learning library for video understanding research.
92&nbsp;<a href="https://github.com/facebookresearch/pytorchvideo"><img alt="GitHub stars" src="https://img.shields.io/github/stars/facebookresearch/pytorchvideo?style=social"></a>
93                                    </li>
94                                    <br/>
95                                </td>
96                              </tr>
97                            </table>
98
99
100
101                            <! –– ICCV Tutorial––>
102                            <table>
103                            <tr>
104                                <td align="left" bgcolor="" width = "1000px" >
105                                    <li> <b>Jun 2020</b>: Co-organized tutorials on Visual Recognition for Images, Video, and 3D at <a href = "http://s9xie.github.io/Tutorials/CVPR2020/" target="_blank">CVPR 2020</a> and  <a href = "http://s9xie.github.io/Tutorials/ECCV2020/" target="_blank">ECCV 2020</a>.</li>
106                                    <br/>
107                                </td>
108                              </tr>
109                            </table>
110
111
112
113                            <! –– PySlowFast Release––>
114                            <table>
115                            <tr>
116                                <td align="left" bgcolor="" width = "1000px" >
117                                    <li> <b>Nov 2019</b>: Released <a href="https://github.com/facebookresearch/SlowFast">PySlowFast</a> codebase for video understanding research.&nbsp;<a href="https://github.com/facebookresearch/SlowFast"><img alt="GitHub stars" src="https://img.shields.io/github/stars/facebookresearch/SlowFast?style=social"></a></li>
118                                    <br/>
119                                </td>
120                              </tr>
121                            </table>
122
123                            <! –– ICCV Tutorial––>
124                            <table>
125                            <tr>
126                                <td align="left" bgcolor="" width = "1000px" >
127                                    <li> <b>Nov 2019</b>: Co-organized a tutorial on <a href = "https://alexander-kirillov.github.io/tutorials/visual-recognition-iccv19/" target="_blank">Images, Video, and 3D</a> research and code at ICCV 2019.</li>
128                                    <br/>
129                                </td>
130                              </tr>
131                            </table>
132
133                            <! –– AVA Win––>
134                            <table>
135                            <tr>
136                                <td align="left" bgcolor="" width = "1000px" >
137                                    <li> <b>June 2019</b>: Won the 1st place of <a href = "http://research.google.com/ava/challenge.html" target="_blank">AVA video activity detection challenge</a> at the <a href = "http://activity-net.org/challenges/2019/" target="_blank">International Challenge on Activity Recognition (ActvityNet) ICCV 2019</a>.</li>
138                                    <br/>
139                                </td>
140                              </tr>
141                            </table>
142
143                            <!--
144                            <! –– Outstanding Reviewer––>
145                            <table>
146                            <tr>
147                                <td align="left" bgcolor="" width = "1000px" >
148                                    <li> <b>June 2019</b>: Selected as CVPR 2019 <a href = "https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8954223" target="_blank">Outstanding Reviewer</a>.</li>
149                                    <br/>
150                                </td>
151                              </tr>
152                            </table>
153                            -->
154
155
156                            <h3>Selected Publications</h3>
157
158                            <! –– BAGEL ––>
159                            <table>
160                            <tr>
161                                <td align="left" width = "50px"></td>
162                                <td align="left" width = "350px">
163                                    <img class="image" src="image/bagel-promo-vid.gif" width = "250px" alt="" />
164                                </td>
165                                <td align="left" bgcolor="" width = "600px" >
166
167                                    <b>Emerging Properties in Unified Multimodal Pretraining</b> <br/>
168                                    Chaorui Deng∗, Deyao Zhu∗, Kunchang Li∗, Chenhui Gou∗, Feng Li∗, 
169                                    Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, Guang Shi§, 
170                                    <b>Haoqi Fan∗†
171                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2505.14683" target="_blank" rel="noopener">
172                                        <i class="fas fa-file-pdf mr-1"></i> Paper
173                                    </a>
174                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://bagel-ai.org/" target="_blank" rel="noopener">
175                                        <i class="fas fa-code mr-1"></i>
176                                        website
177                                    </a>
178                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/bytedance-seed/BAGEL" target="_blank" rel="noopener">
179                                        <i class="fas fa-code mr-1"></i>
180                                       code 
181                                    </a>
182                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://huggingface.co/ByteDance-Seed/BAGEL-7B-MoT" target="_blank" rel="noopener">
183                                        <i class="fas fa-code mr-1"></i>
184                                       huggingface
185                                    </a>
186                                    <br/>
187                                </td>
188                              </tr>
189                            </table>
190
191
192                            <! –– FLIP ––>
193                            <table>
194                            <tr>
195                                <td align="left" width = "50px"></td>
196                                <td align="left" width = "350px">
197                                    <img class="image" src="image/flip.png" width = "250px" alt="" />
198                                </td>
199                                <td align="left" bgcolor="" width = "600px" >
200
201                                    <b>Scaling Language-Image Pre-training via Masking</b> <br/>
202                                    Yanghao Li*, <b>Haoqi Fan</b>*, Ronghang Hu*, Christoph Feichtenhofer†, Kaiming He†<br/>
203                                    <i>Arxiv</i>, 2022<br/>
204                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2212.00794" target="_blank" rel="noopener">
205                                        <i class="fas fa-file-pdf mr-1"></i> Paper
206                                    </a>
207                                    <br/>
208
209                                </td>
210                              </tr>
211                            </table>
212
213
214                            <! –– Video MAE ––>
215                            <table>
216                            <tr>
217                                <td align="left" width = "50px"></td>
218                                <td align="left" width = "350px">
219                                    <img class="image" src="image/mae_st.png" width = "250px" alt="" />
220                                </td>
221                                <td align="left" bgcolor="" width = "600px" >
222
223                                    <b>Masked Autoencoders As Spatiotemporal Learners</b> <br/>
224                                    Christoph Feichtenhofer*, <b>Haoqi Fan</b>*, Yanghao Li, Kaiming He<br/>
225                                    <i>NeurIPS</i>, 2022<br/>
226                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2205.09113" target="_blank" rel="noopener">
227                                        <i class="fas fa-file-pdf mr-1"></i> Paper
228                                    </a>
229                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/mae_st" target="_blank" rel="noopener">
230                                        <i class="fas fa-code mr-1"></i>
231                                        code
232                                    </a>
233                                    <br/>
234
235                                </td>
236                              </tr>
237                            </table>
238
239
240
241                            <! –– MViT ––>
242                            <table>
243                            <tr>
244                                <td align="left" width = "50px"></td>
245                                <td align="left" width = "350px">
246                                    <img class="image" src="image/mvit-img-det-vid.png" width = "250px" alt="" />
247                                </td>
248                                <td align="left" bgcolor="" width = "600px" >
249
250                                    <b>Multiscale Vision Transformers</b> <br/>
251                                    <b>Haoqi Fan*</b>, Bo Xiong*, Karttikeya Mangalam*, Yanghao Li*, Zhicheng Yan, Jitendra Malik, Christoph Feichtenhofer<br/>
252                                    <i>ICCV</i>, 2021<br/>
253                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2104.11227" target="_blank" rel="noopener">
254                                        <i class="fas fa-file-pdf mr-1"></i> Paper
255                                    </a>
256                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/SlowFast" target="_blank" rel="noopener">
257                                        <i class="fas fa-code mr-1"></i>
258                                        code
259                                    </a>
260                                    <br/>
261
262                                    <b>Improved Multiscale Vision Transformers for Classification and Detection</b> <br/>
263                                    Yanghao Li, Chao-Yuan Wu, <b>Haoqi Fan</b>, Karttikeya Mangalam, Bo Xiong, Jitendra Malik, Christoph Feichtenhofer<br/>
264                                    <i>Tech Report</i>, 2021<br/>
265                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2112.01526" target="_blank" rel="noopener">
266                                        <i class="fas fa-file-pdf mr-1"></i> Paper
267                                    </a>
268                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/SlowFast" target="_blank" rel="noopener">
269                                        <i class="fas fa-code mr-1"></i>
270                                        code
271                                    </a>
272                                    <br/>
273
274
275
276                                </td>
277                              </tr>
278                            </table>
279
280
281
282                            <! –– MoCo Video ––>
283                            <!--
284                            <table>
285                            <tr>
286                                <td align="left" width = "50px"></td>
287                                <td align="left" width = "350px">
288                                    <img class="image" src="image/videomoco.png" width = "250px" alt="" />
289                                    <img class="image" src="image/videomoco_output.png" width = "250px" alt="" />
290                                </td>
291                                <td align="left" bgcolor="" width = "600px" >
292                                    <b>A Large-Scale Study on Unsupervised Spatiotemporal Representation Learning</b> <br/>
293                                    Christoph Feichtenhofer, <
293b>Haoqi Fan</b>, Bo Xiong, Ross Girshick, Kaiming He<br/>
294                                    <i>Conference on Computer Vision and Pattern Recognition (CVPR)</i>, 2021<br/>
295                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2104.14558" target="_blank" rel="noopener">
296                                        <i class="fas fa-file-pdf mr-1"></i> Paper
297                                    </a>
298                                    <br/>
299                                </td>
300                              </tr>
301                            </table>
302                            -->
303
304
305                            <!--
306                            <! –– Collaborative Mem ––>
307                            <table>
308                            <tr>
309                                <td align="left" width = "50px"></td>
310                                <td align="left" width = "350px"><img class="image" src="image/c_mem.png" width = "250px" alt="" /></td>
311                                <td align="left" bgcolor="" width = "600px" >
312                                    <b>Beyond Short Clips: End-to-End Video-Level Learning with Collaborative Memories</b> <br/>
313                                    Xitong Yang, <b>Haoqi Fan</b>, Lorenzo Torresani, Larry Davis, Heng Wang <br/>
314                                    <i>Conference on Computer Vision and Pattern Recognition (CVPR)</i>, 2021<br/>
315                                     <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2104.01198" target="_blank" rel="noopener">
316                                        <i class="fas fa-file-pdf mr-1"></i> Paper
317                                    </a>
318                                    <br/>
319                                </td>
320                              </tr>
321                            </table>
322
323
324                            <! –– multiview video––>
325                            <table>
326                            <tr>
327                                <td align="left" width = "50px"></td>
328                                <td align="left" width = "350px"><img class="image" src="image/multiview_video.png" width = "250px" alt="" /></td>
329                                <td align="left" bgcolor="" width = "600px" >
330                                    <b>Multiview Pseudo-Labeling for Semi-supervised Learning from Video</b> <br/>
331                                    Bo Xiong, <b>Haoqi Fan</b>, Kristen Grauman, Christoph Feichtenhofer<br/>
332                                    <i>Tech Report</i>, 2021<br/>
333                                     <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2104.00682" target="_blank" rel="noopener">
334                                        <i class="fas fa-file-pdf mr-1"></i> Paper
335                                    </a>
336                                    <br/>
337                                </td>
338                              </tr>
339                            </table>
340                            -->
341
342
343
344
345
346
347                            <! –– MoCo v2 ––>
348                            <!--
349                            <table>
350                            <tr>
351                                <td align="left" width = "50px"></td>
352                                <td align="left" width = "350px"><img class="image" src="image/moco_v2.png" width = "250px" alt="" /></td>
353                                <td align="left" bgcolor="" width = "600px" >
354                                    <b>Improved Baselines with Momentum Contrastive Learning</b> <br/>
355                                    Xinlei Chen, <b>Haoqi Fan</b>, Ross Girshick, Kaiming He <br/>
356                                    <i>2-Page Tech Report</i>, 2020<br/>
357                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="http://arxiv.org/abs/2003.04297" target="_blank" rel="noopener">
358                                        <i class="fas fa-file-pdf mr-1"></i> Paper
359                                    </a>
360                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/moco" target="_blank" rel="noopener">
361                                        <i class="fas fa-code mr-1"></i>
362                                        code
363                                    </a>
364                                    <br/>
365                                </td>
366                              </tr>
367                            </table>
368                            -->
369
370
371
372                            <! –– MoCo ––>
373                            <table>
374                            <tr>
375                                <td align="left" width = "50px"></td>
376                                <td align="left" width = "350px"><img class="image" src="image/moco_v1.png" width = "250px" alt="" /></td>
377                                <td align="left" bgcolor="" width = "600px" >
378                                    <b>Momentum contrast for unsupervised visual representation learning</b> <br/>
379                                    Kaiming He, <b>Haoqi Fan</b>, Yuxin Wu, Saining Xie, Ross Girshick <br/>
380                                    <i>Conference on Computer Vision and Pattern Recognition</i> (CVPR), 2020 (Oral)<br/>
381                                    <i style="color:red">Best Paper Nomination</i><br/>
382                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/1911.05722" target="_blank" rel="noopener">
383                                        <i class="fas fa-file-pdf mr-1"></i> Paper
384                                    </a>
385                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/moco" target="_blank" rel="noopener">
386                                        <i class="fas fa-code mr-1"></i>
387                                        code
388                                    </a>
389                                    <br/>
390                                    <b>Improved Baselines with Momentum Contrastive Learning</b> <br/>
391                                    Xinlei Chen, <b>Haoqi Fan</b>, Ross Girshick, Kaiming He <br/>
392                                    <i>2-Page Tech Report</i>, 2020<br/>
393                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="http://arxiv.org/abs/2003.04297" target="_blank" rel="noopener">
394                                        <i class="fas fa-file-pdf mr-1"></i> Paper
395                                    </a>
396                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/moco" target="_blank" rel="noopener">
397                                        <i class="fas fa-code mr-1"></i>
398                                        code
399                                    </a>
400                                    <br/>
401                                    <b>On the Importance of Asymmetry for Siamese Representation Learning</b> <br/>
402                                    Xiao Wang*, <b>
402Haoqi Fan*</b>, Yuandong Tian, Daisuke Kihara, Xinlei Chen<br/>
403                                    <i>CVPR</i>, 2022<br/>
404                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="http://arxiv.org/abs/2204.00613" target="_blank" rel="noopener">
405                                        <i class="fas fa-file-pdf mr-1"></i> Paper
406                                    </a>
407                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/asym-siam" target="_blank" rel="noopener">
408                                        <i class="fas fa-code mr-1"></i>
409                                        code
410                                    </a>
411                                    <br/>
412                                </td>
413                              </tr>
414                            </table>
415
416                           
417                            <! –– SlowFast ––>
418                            <table>
419                            <tr>
420                                <td align="left" width = "50px"></td>
421                                <td align="left" width = "350px"><img class="image" src="image/slowfast.gif" width = "250px" alt="" /></td>
422                                <td align="left" bgcolor="" width = "600px" >
423                                    <b>Slowfast networks for video recognition</b> <br/>
424                                    Christoph Feichtenhofer, <b>Haoqi Fan</b>, Jitendra Malik, Kaiming He <br/>
425                                    <i>International Conference on Computer Vision</i> (ICCV), 2019 (Oral)<br/>
426                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/1812.03982" target="_blank" rel="noopener">
427                                        <i class="fas fa-file-pdf mr-1"></i> Paper
428                                    </a>
429                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/SlowFast" target="_blank" rel="noopener">
430                                        <i class="fas fa-code mr-1"></i>
431                                        code
432                                    </a>
433                                    <br/>
434                                </td>
435                              </tr>
436                            </table>
437
438
439 
440                            <!--
441                            <! –– OctConv––>
442                            <table>
443                            <tr>
444                                <td align="left" width = "50px"></td>
445                                <td align="left" width = "350px"><img class="image" src="image/octconv.png" width = "250px" alt="" /></td>
446                                <td align="left" bgcolor="" width = "600px" >
447                                    <b>Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution</b> <br/>
448                                    Yunpeng Chen, <b>Haoqi Fan</b>, Bing Xu, Zhicheng Yan, Yannis Kalantidis, Marcus Rohrbach, Shuicheng Yan, Jiashi Feng <br/>
449                                    <i>International Conference on Computer Vision</i> (ICCV), 2019 <br/>
450                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/1904.05049" target="_blank" rel="noopener">
451                                        <i class="fas fa-file-pdf mr-1"></i> Paper
452                                    </a>
453                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/OctConv" target="_blank" rel="noopener">
454                                        <i class="fas fa-code mr-1"></i>
455                                        code
456                                    </a>
457                                    <br/>
458                                </td>
459                              </tr>
460                            </table>
461                            -->
462
463                             
464                            <!--
465                            <! –– 3D dataset––>
466                            <table>
467                            <tr>
468                                <td align="left" width = "50px"></td>
469                                <td align="left" width = "350px"><img class="image" src="image/3d_dataset.png" width = "250px" alt="" /></td>
470                                <td align="left" bgcolor="" width = "600px" >
471                                    <b>
471Order-Aware Generative Modeling Using the 3D-Craft Dataset</b> <br/>
472                                    Zhuoyuan Chen∗ , Demi Guo∗ , Tong Xiao∗ , Saining Xie, Xinlei Chen, Haonan Yu, Jonathan Gray, Kavya Srinet, <b>Haoqi Fan</b>, Jerry Ma, Charles R. Qi, Shubham Tulsiani, Arthur Szlam, and C. Lawrence Zitnick <br/>
473                                    <i>International Conference on Computer Vision</i> (ICCV), 2019 <br/>
474                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="http://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_Order-Aware_Generative_Modeling_Using_the_3D-Craft_Dataset_ICCV_2019_paper.pdf" target="_blank" rel="noopener">
475                                        <i class="fas fa-file-pdf mr-1"></i> Paper
476                                    </a>
477                                    <br/>
478                                </td>
479                              </tr>
480                            </table>
481                            -->
482
483
484 
485                            <!--
486                            <! –– LFB ––>
487                            <table>
488                            <tr>
489                                <td align="left" width = "50px"></td>
490                                <td align="left" width = "350px"><img class="image" src="image/lfb.png" width = "250px" alt="" /></td>
491                                <td align="left" bgcolor="" width = "600px" >
492                                    <b>Long-Term Feature Banks for Detailed Video Understanding</b> <br/>
493                                    Chao-Yuan Wu, Christoph Feichtenhofer, <b>Haoqi Fan</b>, Kaiming He, Philipp Krähenbühl, Ross Girshick <br/>
494                                    <i>Conference on Computer Vision and Pattern Recognition</i> (CVPR), 2019 (Oral)<br/>
495                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/1812.05038" target="_blank" rel="noopener">
496                                        <i class="fas fa-file-pdf mr-1"></i> Paper
497                                    </a>
498                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://github.com/facebookresearch/video-long-term-feature-banks" target="_blank" rel="noopener">
499                                        <i class="fas fa-code mr-1"></i>
500                                        code
501                                    </a>
502                                    <br/>
503                                </td>
504                              </tr>
505                            </table>
506                            -->
507
508 
509                            <! –– sla––>
510                            <table>
511                            <tr>
512                                <td align="left" width = "50px"></td>
513                                <td align="left" width = "350px"><img class="image" src="image/sla.png" width = "250px" alt="" /></td>
514                                <td align="left" bgcolor="" width = "600px" >
515                                    <b>Stacked Latent Attention for Multimodal Reasoning</b> <br/>
516                                    <b>Haoqi Fan</b>, Jiatong Zhou <br/>
517                                    <i>Conference on Computer Vision and Pattern Recognition</i> (CVPR), 2018 <br/>
518                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="http://openaccess.thecvf.com/content_cvpr_2018/papers/Fan_Stacked_Latent_Attention_CVPR_2018_paper.pdf" target="_blank" rel="noopener">
519                                        <i class="fas fa-file-pdf mr-1"></i> Paper
520                                    </a>
521                                    <br/>
522                                </td>
523                              </tr>
524                            </table>
525
526                            <!--
527                            <! –– k shot––>
528                            <table>
529                            <tr>
530                                <td align="left" width = "50px"></td>
531                                <td align="left" width = "350px"><img class="image" src="image/k_shot.png" width = "250px" alt="" /></td>
532                                <td align="left" bgcolor="" width = "600px" >
533                                    <b>Efficient K-Shot Learning with Regularized Deep Networks</b> <br/>
534                                    Donghyun Yoo, <b>Haoqi Fan</b>, Vishnu Naresh Boddeti, Kris M. Kitani <br/>
535                                    <i>Thirty-Second AAAI Conference on Artificial Intelligence</i> (AAAI), 2018 <br/>
536                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/1710.02277" target="_blank" rel="noopener">
537                                        <i class="fas fa-file-pdf mr-1"></i> Paper
538                                    </a>
539                                    <br/>
540                                </td>
541                              </tr>
542                            </table>
543                            --> 
544
545                            <! –– ego––>
546                            <table>
547                            <tr>
548                                <td align="left" width = "50px"></td>
549                                <td align="left" width = "350px"><img class="image" src="image/ego.png" width = "250px" alt="" /></td>
550                                <td align="left" bgcolor="" width = "600px" >
551                                    <b>Going deeper into first-person activity recognition</b> <br/>
552                                    Minghuang Ma, <b>Haoqi Fan</b>, Kris M. Kitani <br/>
553                                    <i>Conference on Computer Vision and Pattern Recognition</i> (CVPR), 2016 <br/>
554                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Ma_Going_Deeper_into_CVPR_2016_paper.pdf" target="_blank" rel="noopener">
555                                        <i class="fas fa-file-pdf mr-1"></i> Paper
556                                    </a>
557                                    <br/>
558                                </td>
559                              </tr>
560                            </table>
561
562
563                            <h3>Open Source Projects</h3>
564
565
566
567                            <! –– BAGEL––>
568                            <table>
569                            <tr>
570                                <td align="left" width = "50px"></td>
571                                <td align="left" width = "350px"><img class="image" src="image/bagel-promo-vid.gif" width = "250px" alt="" /></td>
572                                <td align="left" bgcolor="" width = "600px" >
573                                    <b>
573BAGEL: The Open-Source Unified Multimodal Model</b> <br/>
574                                    Chaorui Deng∗, Deyao Zhu∗, Kunchang Li∗, Chenhui Gou∗, Feng Li∗, 
575                                    Zeyu Wang, Shu Zhong, Weihao Yu, Xiaonan Nie, Ziang Song, Guang Shi§, 
576                                    <b>Haoqi Fan∗†
577                                    <a href="https://github.com/bytedance-seed/BAGEL"><img alt="GitHub stars" src="https://img.shields.io/github/stars/bytedance-seed/BAGEL?style=social"></a></li>
578                                    <br/>
579                                </td>
580                              </tr>
581                            </table>
582
583
584                            <! –– PySlowFast––>
585                            <table>
586                            <tr>
587                                <td align="left" width = "50px"></td>
588                                <td align="left" width = "350px"><img class="image" src="image/pyslowfast.gif" width = "250px" alt="" /></td>
589                                <td align="left" bgcolor="" width = "600px" >
590                                    <b>PySlowFast: video understanding codebase for state-of-the-art research</b> <br/>
591                                    <b>Haoqi Fan</b>, Yanghao Li, Wan-Yen Lo, Christoph Feichtenhofer<br/>
592
593                                    <a href="https://github.com/facebookresearch/SlowFast"><img alt="GitHub stars" src="https://img.shields.io/github/stars/facebookresearch/SlowFast?style=social"></a></li>
594                                    <br/>
595                                </td>
596                              </tr>
597                            </table>
598
599
600                            <! –– PyTorchVideo ––>
601                            <table>
602                            <tr>
603                                <td align="left" width = "50px"></td>
604                                <td align="left" width = "350px"><img style="margin-left:3.5em" class="image" src="image/ptv-teaser.gif" width = "125px" alt="" /></td>
605                                <td align="left" bgcolor="" width = "600px" >
606                                    <b>PyTorchVideo: A Deep Learning Library for Video Understanding</b> <br/>
607                                    <b>Haoqi Fan *</b>, Tullie Murrell *, <br> Heng Wang <span>&#8225;</span>, Kalyan Vasudev Alwala<span>&#8225;</span>, Yanghao Li<span>&#8225;</span>, Yilei Li<span>&#8225;</span>, Bo Xiong <span>&#8225;</span>, <br> Nikhila Ravi, Meng Li, Haichuan Yang, Jitendra Malik, Ross Girshick, Matt Feiszli, <br> Aaron Adcock<span>&#8224;</span>, Wan-Yen Lo<span>&#8224;</span>, Christoph Feichtenhofer <span>&#8224;</span><br/>
608
609                                    <a href="https://github.com/facebookresearch/pytorchvideo"><img alt="GitHub stars" src="https://img.shields.io/github/stars/facebookresearch/pytorchvideo?style=social"></a></li>
610                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://arxiv.org/abs/2111.09887?context=cs.LG" target="_blank" rel="noopener">
611                                         <i class="fas fa-file-pdf mr-1"></i> Paper
612                                     </a>
613                                    <a class="btn btn-outline-primary my-1 mr-1 btn-sm" href="https://ai.facebook.com/blog/pytorchvideo-a-deep-learning-library-for-video-understanding/" target="_blank" rel="noopener">
614                                         <i class="fas fa-file-pdf mr-1"></i> Post
615                                     </a>
616                                    <br/>
617                                </td>
618                              </tr>
619                            </table>
620
621
622                    </div>
623                </div>
624            </div>
625        </div>
626
627        <div id="footer">
628
629        </div>
630
631
632
633  <div class="container">
634    <footer class="site-footer">
635
636
637  <p class="powered-by">
638
639    © 2016 - 2021 · Haoqi Fan
640
641
642
643
644    <span class="float-right" aria-hidden="true">
645      <a href="#" id="back_to_top">
646        <span class="button_icon">
647          <i class="fas fa-chevron-up fa-2x"></i>
648        </span>
649      </a>
650    </span>
651
652  </p>
653</footer>
654
655  </div>
656
657<script>
vendor: 351 bytes, lines 657-663
657
658  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
659  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
660  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
661  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
662
663  ga('create', '
663UA-88566694-1
vendor: 42 bytes, lines 663-665
663', 'auto');
664  ga('send', 'pageview');
665
666</script>
666        
667
668
669<script src="js/jquery.min.js"></script>
vendor: 2 bytes, line 669
669
670<script src="js/tether.min.js"></script>
vendor: 2 bytes, line 670
670
671<script src="js/bootstrap.min.js"></script>
vendor: 2 bytes, line 671
671
672<script>
vendor: 351 bytes, lines 672-678
672
673  (function(i,s,o,g,r,a,m){i['GoogleAnalyticsObject']=r;i[r]=i[r]||function(){
674  (i[r].q=i[r].q||[]).push(arguments)},i[r].l=1*new Date();a=s.createElement(o),
675  m=s.getElementsByTagName(o)[0];a.async=1;a.src=g;m.parentNode.insertBefore(a,m)
676  })(window,document,'script','https://www.google-analytics.com/analytics.js','ga');
677
678  ga('create', '
678UA-89924514-1
vendor: 42 bytes, lines 678-680
678', 'auto');
679  ga('send', 'pageview');
680
681</script>
681
682
683<script type="text/javascript" src="//rf.revolvermaps.com/0/0/8.js?i=550ynja4lex&amp;m=0c&amp;c=ff8a00&amp;cr1=ff0000&amp;f=arial&amp;l=33&amp;cw=ffffff&amp;cb=007eff" async="async"></script>
683
684
685
686    </body>
687</html>

Line numbers count LF bytes from the start of the resource, as the search results do. Vendor segments are library code the classifier recognised; they are stored but not indexed. Bytes are shown as Latin1 characters, one per byte.