generated from eliahuhorwitz/Academic-project-page-template
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathindex.html
315 lines (274 loc) · 14.8 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
<!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="Net:Cal - Bayesian Confidence Calibration for Epistemic Uncertainty Modelling">
<meta property="og:title" content="Bayesian Confidence Calibration for Epistemic Uncertainty Modelling"/>
<meta property="og:description" content="Presenting a framework to enhance accuracy in object detection through innovative bayesian calibration of confidence estimates, addressing a gap in current methods." />
<meta property="og:url" content="https://trustinai.github.io/bconfcal/"/>
<!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X630-->
<meta property="og:image" content="https://trustinai.github.io/bconfcal/static/image/twitter_banner.png" />
<meta property="og:image:width" content="1200"/>
<meta property="og:image:height" content="630"/>
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@TrustInAILab" />
<meta name="twitter:url" content="https://trustinai.github.io/bconfcal/" />
<meta name="twitter:title" content="net:cal - Bayesian Confidence Calibration for Epistemic Uncertainty Modelling" />
<meta name="twitter:description" content="Presenting a framework to enhance accuracy in object detection through innovative bayesian calibration of confidence estimates, addressing a gap in current methods." />
<meta name="twitter:image" content="https://trustinai.github.io/bconfcal/static/images/twitter_banner.png" />
<meta name="keywords" content="#IV #ObjectDetection #AI #NetCal #UncertaintyQuantification #UncertaintyCalibration #ConfidenceCalibration"/>
<!--
<meta name="twitter:title" content="net:cal - Multivariate Confidence Calibration for Object Detection">
<meta name="twitter:description" content="Introducing a novel framework to boost object detection accuracy by calibrating confidence estimates using regression data, filling a crucial gap. Achieves calibrated confidence for different image locations and scales, outperforming existing models.">
-->
<!-- Path to banner image, should be in the path listed below. Optimal dimenssions are 1200X600
<meta name="twitter:image" content="static/images/twitter_banner.png">
<meta name="twitter:card" content="summary_large_image">
-->
<!-- Keywords for your paper to be indexed by
<meta name="keywords" content="#CVPRW #SAIAD #ObjectDetection #AI #NetCal #UncertaintyQuantification #UncertaintyCalibration #ConfidenceCalibration">
<meta name="viewport" content="width=device-width, initial-scale=1">
-->
<title>Bayesian Confidence Calibration for Epistemic Uncertainty Modelling</title>
<link rel="icon" type="image/x-icon" href="static/images/favicon.ico">
<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">Bayesian Confidence Calibration for Epistemic Uncertainty Modelling</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://www.linkedin.com/in/fabian-küppers-726353201/" target="_blank">Fabian Küppers</a><sup>1</sup>,</span>
<span class="author-block">
<a href="" target="_blank">Jan Kronenberger</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://www.linkedin.com/in/jonas-schneider-ai/" target="_blank">Jonas Schneider</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://trustin.ai" target="_blank">Anselm Haselhoff</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><small><sup>1</sup>Ruhr West University of Applied Sciences, Germany; <sup>2</sup>e:fs TechHub GmbH, Germany</small></span>
<span class="author-block">Intelligent Vehicles Symposium (IV), 2021</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Arxiv PDF link -->
<span class="link-block">
<a href="https://ieeexplore.ieee.org/document/9575841" 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/EFS-OpenSource/calibration-framework" 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/2109.10092" 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 video-->
<!--
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<video poster="" id="tree" autoplay controls muted loop height="100%">
<source src="static/videos/banner_video.mp4"
type="video/mp4">
</video>
<h2 class="subtitle has-text-centered">
Aliquam vitae elit ullamcorper tellus egestas pellentesque. Ut lacus tellus, maximus vel lectus at, placerat pretium mi. Maecenas dignissim tincidunt vestibulum. Sed consequat hendrerit nisl ut maximus.
</h2>
</div>
</div>
</section>
-->
<!-- End teaser video -->
<!-- Paper abstract -->
<section class="section hero 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-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Modern neural networks have found to be miscalibrated in terms of confidence calibration, i.e., their predicted confidence scores do not reflect the observed accuracy or precision. Recent work has introduced methods for post-hoc confidence calibration for classification as well as for object detection to address this issue. Especially in safety critical applications, it is crucial to obtain a reliable self-assessment of a model. But what if the calibration method itself is uncertain, e.g., due to an insufficient knowledge base?
We introduce Bayesian confidence calibration - a framework to obtain calibrated confidence estimates in conjunction with an uncertainty of the calibration method. Commonly, Bayesian neural networks (BNN) are used to indicate a network's uncertainty about a certain prediction. BNNs are interpreted as neural networks that use distributions instead of weights for inference. We transfer this idea of using distributions to confidence calibration. For this purpose, we use stochastic variational inference to build a calibration mapping that outputs a probability distribution rather than a single calibrated estimate. Using this approach, we achieve state-of-the-art calibration performance for object detection calibration. Finally, we show that this additional type of uncertainty can be used as a sufficient criterion for covariate shift detection.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
<!-- Image carousel -->
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<div id="results-carousel" class="carousel results-carousel">
<div class="item">
<!-- Your image here -->
<img src="static/images/carousel1.png" alt="Stochastic variational inference (SVI) for calibration"/>
<h2 class="subtitle has-text-centered">
Using stochastic variational inference (SVI), we are able to obtain not only a single calibrated estimate but also an additional prediction interval quantifying the epistemic uncertainty within the calibration mapping. We use a position-dependent calibration framework, but place distributions over the calibration parameters to infer a sample distribution for a single prediction.
</h2>
</div>
<div class="item">
<!-- Your image here -->
<img src="static/images/carousel2.png" alt="Stochastic variational inference (SVI) for calibration"/>
<h2 class="subtitle has-text-centered">
An object detection model outputs a confidence estimate attached to each bounding box with a certain position and shape. This information is used for position & scale dependent confidence calibration. Instead of maximum likelihood estimation, we utilize stochastic variational inference to predict a sample distribution for each detection. On the one hand, this sample distribution reflects the observed frequency and on the other hand the epistemic uncertainty of the calibration model for a certain confidence, position and shape. Using highest density interval estimation, it is thus possible to denote a prediction interval for each calibrated estimate.
</h2>
</div>
</div>
</div>
</div>
</section>
<!-- End image carousel -->
<!-- Youtube video
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Video Presentation</h2>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="publication-video">
<iframe width="560" height="315" src="https://www.youtube.com/embed/g4VuFAFIeE0?si=LARY39yNyBPJ6rDV" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div>
</div>
</div>
</div>
</div>
</section>
-->
<!-- Video carousel
<section class="hero is-small">
<div class="hero-body">
<div class="container">
<h2 class="title is-3">Another Carousel</h2>
<div id="results-carousel" class="carousel results-carousel">
<div class="item item-video1">
<video poster="" id="video1" autoplay controls muted loop height="100%">
<source src="static/videos/carousel1.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video2">
<video poster="" id="video2" autoplay controls muted loop height="100%">
<source src="static/videos/carousel2.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-video3">
<video poster="" id="video3" autoplay controls muted loop height="100%">\
<source src="static/videos/carousel3.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</section>
-->
<!-- End video carousel -->
<!-- Paper poster
<section class="hero is-small is-light">
<div class="hero-body">
<div class="container">
<h2 class="title">Poster</h2>
<iframe src="static/pdfs/sample.pdf" width="100%" height="550">
</iframe>
</div>
</div>
</section>
-->
<!--End paper poster -->
<!--BibTex citation -->
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@InProceedings{Kueppers_2021_IV,
author = {Küppers, Fabian and Kronenberger, Jan and Schneider, Jonas and Haselhoff, Anselm},
title = {Bayesian Confidence Calibration for Epistemic Uncertainty Modelling},
booktitle = {Proceedings of the IEEE Intelligent Vehicles Symposium (IV)},
month = {July},
year = {2021},
}</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> which was adopted from the <a href="https://nerfies.github.io" target="_blank">Nerfies</a> project page.
<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 -->
<!-- You can add a tracker to track page visits by creating an account at statcounter.com -->
<!-- End of Statcounter Code -->
</body>
</html>