generated from eliahuhorwitz/Academic-project-page-template
-
Notifications
You must be signed in to change notification settings - Fork 0
/
index.html
399 lines (344 loc) · 16 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<!-- Meta tags for social media banners, these should be filled in appropriatly as they are your "business card" -->
<!-- Replace the content tag with appropriate information -->
<meta name="description" content="DESCRIPTION META TAG">
<meta property="og:title" content="SOCIAL MEDIA TITLE TAG" />
<meta property="og:description" content="SOCIAL MEDIA DESCRIPTION TAG TAG" />
<meta property="og:url" content="URL OF THE WEBSITE" />
<!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X630-->
<meta property="og:image" content="static/image/your_banner_image.png" />
<meta property="og:image:width" content="1200" />
<meta property="og:image:height" content="630" />
<meta name="twitter:title" content="TWITTER BANNER TITLE META TAG">
<meta name="twitter:description" content="TWITTER BANNER DESCRIPTION META TAG">
<!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X600-->
<meta name="twitter:image" content="static/images/your_twitter_banner_image.png">
<meta name="twitter:card" content="summary_large_image">
<!-- Keywords for your paper to be indexed by-->
<meta name="keywords" content="KEYWORDS SHOULD BE PLACED HERE">
<meta name="viewport" content="width=device-width, initial-scale=1">
<title>RbA: Segmenting Unknown Regions Rejected by All</title>
<link rel="icon" type="image/x-icon" href="static/images/KUIS_AI_LOGO_2-vectorimage.ico">
<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro" rel="stylesheet">
<link rel="stylesheet" href="static/css/bulma.min.css">
<link rel="stylesheet" href="static/css/bulma-carousel.min.css">
<link rel="stylesheet" href="static/css/bulma-slider.min.css">
<link rel="stylesheet" href="static/css/fontawesome.all.min.css">
<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css">
<link rel="stylesheet" href="static/css/index.css">
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
<script src="https://documentcloud.adobe.com/view-sdk/main.js"></script>
<script defer src="static/js/fontawesome.all.min.js"></script>
<script src="static/js/bulma-carousel.min.js"></script>
<script src="static/js/bulma-slider.min.js"></script>
<script src="static/js/index.js"></script>
</head>
<body>
<section class="hero">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">RbA: Segmenting Unknown Regions Rejected by All</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://nazirnayal.xyz/" target="_blank">Nazir Nayal</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=lfU8AYUAAAAJ&hl=en" target="_blank">Mısra
Yavuz</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://www.robots.ox.ac.uk/~joao/" target="_blank">Joao F. Henriques</a> <sup>2</sup></span>
<span class="author-block">
<a href="https://mysite.ku.edu.tr/fguney/" target="_blank">Fatma Güney</a> <sup>1</sup></span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>KUIS AI Center and Department of Computer Engineering, Koç
University <sup>2</sup>University of
Oxford</span> <br>ICCV 2023
<!-- <span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span> -->
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Arxiv PDF link -->
<span class="link-block">
<a href="https://arxiv.org/pdf/2211.14293.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<!-- Supplementary PDF link
<span class="link-block">
<a href="static/pdfs/supplementary_material.pdf" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Supplementary</span>
</a>
</span> -->
<!-- Github link -->
<span class="link-block">
<a href="https://github.com/NazirNayal8/RbA" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
<!-- ArXiv abstract Link -->
<span class="link-block">
<a href="https://arxiv.org/abs/2211.14293" target="_blank"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Teaser content-->
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="row">
<div class="column">
<!--add the following gif file static/gifs/ra_21_23_rba.gif-->
<!-- <img src="static/gifs/ra_21_23_rba.gif" alt="RbA" width="100%"> -->
<video autoplay loop muted inline>
<source src="static/gifs/rba_teaser.mp4" type="video/mp4">
</video>
</div>
</div>
<div class="box">
<b> <FONT COLOR="RED">TL;DR</FONT></b> Object queries in mask classification behave like one vs. all classifiers.
We experimentally confirm this behavior and reinforce it to find unknown objects as pixels rejected by all known classes.
</div>
</div>
</div>
</section>
<!--End teaser content -->
<!-- Paper abstract -->
<section class="section hero is-small is-light">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-2">Abstract</h2>
<div class="content has-text-justified">
<p>
Standard semantic segmentation models owe their success to curated datasets with a fixed set of semantic
categories,without contemplating the possibility of identifying unknown objects from novel categories.
Existing methods in outlier detection suffer from a lack of smoothness and objectness in their
predictions, due to limitations of the per-pixel classification paradigm. Furthermore, additional training
for detecting outliers harms the performance of known classes. In this paper, we explore another paradigm
with region-level classification to better segment unknown objects. We show that the object queries in
mask classification tend to behave like one vs. all classifiers. Based on this finding, we propose a novel
outlier scoring function called RbA by defining the event of being an outlier as being rejected by all
known classes. Our extensive experiments show that mask classification improves the performance of the
existing outlier detection methods, and the best results are achieved with the proposed RbA.
We also propose an objective to optimize RbA using minimal outlier supervision. Further fine-tuning with
outliers improves the unknown performance, and unlike previous methods, it does not degrade the inlier
performance.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Method Overview -->
<section class="section hero is-small">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="columns is-centered has-text-centered">
<h2 class="title is-2">Method Overview</h2>
</div>
<div class="row">
<div class="column">
<img src="static/images/overview.jpg" alt="MY ALT TEXT" style="width:100%">
</div>
</div>
<h2 class="subtitle has-text-justified " style="font-size : 18px">
Object queries (<i>colored</i> ) in mask classifiers each specialize in detecting a different semantic class,
and the performance of each query is <b>independent</b> of the other queries. Hence, we model each object
query as an independent
one vs. all classifier and consider unknown regions as those rejected by all (RbA) queries. RbA can be
regularized with outlier data
to supress the overconfidence of known class scores for unknown pixels.
</h2>
</div>
</div>
<br>
<hr>
</section>
<!-- One vs. All Behavior -->
<!-- <section class="section hero is-small">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="columns is-centered has-text-centered">
<h2 class="title is-2">One vs. All Behavior of Object Queries</h2>
</div>
<div class="content has-text-justified">
<p>
As the training converges, the object queries in mask classifiers each specialize in segmenting a certain class. This behavior is experimentally confirmed
in the figure below. For a model with 100 object queries (x-axis), and for each of the 19 classes of cityscapes (y-axis), we count how many times each query
predicted each class with high confidence (>98%).
</p>
</div>
<img src="static/images/specialization.jpg" alt="MY ALT TEXT" style="width:100%">
<div class="content has-text-justified">
<p>
This figure allows for identifying the specialized query for each class. A <b>key observation</b> for the success of our method is the <b>independence</b> of
the object queries from one another in their decision making. In the transformer
</p>
</div>
</div>
</div>
</section> -->
<!-- RbA Score -->
<!-- <section class="section hero is-small">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="columns is-centered has-text-centered">
<h2 class="title is-2">Rejected by All (RbA) Score</h2>
</div>
</div>
</div>
</section> -->
<!-- Analysis of Logits Behavior -->
<!-- <section class="section hero is-small">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="columns is-centered has-text-centered">
<h2 class="title is-2">Analysis of Logits Behavior</h2>
</div>
</div>
</div>
</section> -->
<section class="section hero is-small">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="columns is-centered has-text-centered">
<h2 class="title is-2">Applying RbA to Real Life Videos</h2>
</div>
<!-- <img src="static/gifs/real_life_example.mp4" alt="RbA" width="100%"> -->
<video autoplay loop muted inline>
<source src="static/gifs/real_life_example.mp4" type="video/mp4">
</video>
<!-- add caption to the gif, make font smaller-->
<div class="content has-text-centered">
<p style="font-size : 10px">
credit: https://www.youtube.com/watch?v=EOosn78WsMg
</p>
</div>
</div>
</section>
<!-- Qualitative Results -->
<section class="section hero is-small">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="columns is-centered has-text-centered">
<h2 class="title is-2">Qualitative Results</h2>
</div>
<br>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="content has-text-justified">
<p>
We show the qualitative results compared to previous SOTA on SMIYC Anomaly and Obstacle tracks:
</p>
</div>
<br>
</div>
</div>
<div>
<img src="static/quals/smiyc_anomaly_quals_11zon.png" alt="MY ALT TEXT" style="width:100%">
</div>
<div>
<img src="static/quals/smiyc_obstacle_qual_11zon.png" alt="MY ALT TEXT" style="width:100%">
</div>
</div>
</div>
</section>
<!-- Quantitative Results -->
<section class="section hero is-small">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="columns is-centered has-text-centered">
<h2 class="title is-2">Quantitative Results</h2>
</div>
</div>
<!--embed the following page from this website: https://segmentmeifyoucan.com/leaderboard, aligned to the left-->
</div>
<br>
<br>
<div class="columns is-centered">
<iframe src="https://segmentmeifyoucan.com/leaderboard" width="80%" height="800px" align="middle">
</iframe>
</div>
</section>
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>
@InProceedings{nayal2023ICCV,
author = {Nazir Nayal and Mısra Yavuz and João F. Henriques and Fatma Güney},
title = {RbA: Segmenting Unknown Regions Rejected by All},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
year = {2023},
}
</code></pre>
</div>
</section>
<!--End BibTex citation -->
<footer class="footer">
<div class="container">
<div class="columns is-centered">
<div class="column is-8">
<div class="content">
<p>
This page was built using the <a href="https://github.com/eliahuhorwitz/Academic-project-page-template"
target="_blank">Academic Project Page Template</a>.
You are free to borrow the of this website, we just ask that you link back to this page in the footer.
<br> This website is licensed under a <a rel="license"
href="http://creativecommons.org/licenses/by-sa/4.0/" target="_blank">Creative
Commons Attribution-ShareAlike 4.0 International License</a>.
</p>
</div>
</div>
</div>
</div>
</footer>
<!-- Statcounter tracking code -->
<!-- Default Statcounter code for RbA Project
https://nazirnayal8.github.io/RbA-Project/ -->
<script type="text/javascript">
var sc_project = 12903040;
var sc_invisible = 1;
var sc_security = "2110fd4b";
</script>
<script type="text/javascript" src="https://www.statcounter.com/counter/counter.js" async></script>
<noscript>
<div class="statcounter"><a title="Web Analytics" href="https://statcounter.com/" target="_blank"><img
class="statcounter" src="https://c.statcounter.com/12903040/0/2110fd4b/1/" alt="Web Analytics"
referrerPolicy="no-referrer-when-downgrade"></a></div>
</noscript>
<!-- End of Statcounter Code -->
<!-- You can add a tracker to track page visits by creating an account at statcounter.com -->
<!-- End of Statcounter Code -->
</body>
</html>