WIP

bib.bib

@@ -31,11 +31,11 @@
   journal   = {arXiv preprint arXiv:1704.07804},
   year      = {2017}}
 
-@article{MaskRCNN,
+@inproceedings{MaskRCNN,
   Author    = {Kaiming He and Georgia Gkioxari and
                Piotr Doll\'{a}r and Ross Girshick},
   Title     = {{Mask {R-CNN}}},
-  Journal   = {arXiv preprint arXiv:1703.06870},
+  Booktitle   = {CVPR},
   Year      = {2017}}
 
 @inproceedings{FasterRCNN,
@@ -80,19 +80,19 @@
 @article{VGGNet,
     author       = {Karen Simonyan and Andrew Zisserman},
     title        = {Very Deep Convolutional Networks for Large-Scale Image Recognition},
-    journal      = {arXiv preprint arXiv:1409.1556},
-    year         = {2014}}
+    journal      = {ICLR},
+    year         = {2015}}
 
-@article{ResNet,
+@inproceedings{ResNet,
 	author = {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian Sun},
 	title = {Deep Residual Learning for Image Recognition},
-	journal = {arXiv preprint arXiv:1512.03385},
-	year = {2015}}
+	booktitle = {CVPR},
+	year = {2016}}
 
 @article{DenseNetDenseFlow,
   author    = {Yi Zhu and Shawn D. Newsam},
   title     = {DenseNet for Dense Flow},
-  journal   = {arXiv preprint arXiv:1707.06316},
+  journal   = {ICIP},
   year      = {2017}}
 
 @inproceedings{SE3Nets,
@@ -114,10 +114,10 @@
   year = {2016}}
 
 @inproceedings{VKITTI,
-    author = {Adrien Gaidon and Qiao Wang and Yohann Cabon and Eleonora Vig},
-    title = {Virtual Worlds as Proxy for Multi-Object Tracking Analysis},
-    booktitle = {{CVPR}},
-    year = {2016}}
+  author = {Adrien Gaidon and Qiao Wang and Yohann Cabon and Eleonora Vig},
+  title = {Virtual Worlds as Proxy for Multi-Object Tracking Analysis},
+  booktitle = {{CVPR}},
+  year = {2016}}
 
 @inproceedings{KITTI2012,
   author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
@@ -152,14 +152,14 @@
 @article{SPyNet,
   author = {Anurag Ranjan and Michael J. Black},
   title = {Optical Flow Estimation using a Spatial Pyramid Network},
-  journal = {arXiv preprint arXiv:1611.00850},
-  year = {2016}}
+  journal = {CVPR},
+  year = {2017}}
 
-@article{FPN,
+@inproceedings{FPN,
   author = {Tsung-Yi Lin and Piotr Dollár and Ross Girshick and Kaiming He and Bharath Hariharan and Serge Belongie},
   title = {Feature Pyramid Networks for Object Detection},
-  journal = {arXiv preprint arXiv:1612.03144},
-  year = {2016}}
+  booktitle = {CVPR},
+  year = {2017}}
 
 @inproceedings{PoseNet,
   author = {Alex Kendall and Matthew Grimes and Roberto Cipolla},
@@ -190,3 +190,17 @@
   title = {Deeper Depth Prediction with Fully Convolutional Residual Networks},
   booktitle = {3DV},
   year = {2016}}
+
+@inproceedings{TensorFlowObjectDetection,
+  author = {J. Huang and V. Rathod and C. Sun and M. Zhu and A. Korattikara and A. Fathi and I. Fischer and Z. Wojna,
+            and Y. Song and S. Guadarrama and K. Murphy},
+  title = {Speed/accuracy trade-offs for modern convolutional object detectors},
+  booktitle = {CVPR},
+  year = {2017}}
+
+@misc{TensorFlow,
+	title={{TensorFlow}: Large-Scale Machine Learning on Heterogeneous Systems},
+	url={http://tensorflow.org/},
+	note={Software available from tensorflow.org},
+	author={Martín Abadi and others},
+	year={2015}}

experiments.tex

@@ -1,3 +1,18 @@
+\subsection{Implementation}
+Our networks and loss functions are implemented using built-in TensorFlow \cite{TensorFlow}
+functions, enabling us to use automatic differentiation for all gradient
+computations. To make our code easy to extend and flexible, we build on
+the TensorFlow Object detection API \cite{TensorFlowObjectDetection}, which provides a Faster R-CNN baseline
+implementation.
+On top of this, we implemented Mask R-CNN and the Feature Pyramid Network (FPN)
+as well all extensions for motion estimation and related evaluations
+and postprocessings. In addition, we generated all ground truth for
+Motion R-CNN in the form of TFRecords from the raw Virtual KITTI
+data to enable fast loading during training.
+Note that for RoI pooling and cropping,
+we use the \texttt{tf.crop\_and\_resize} TensorFlow function with
+interpolation set to bilinear.
+
 \subsection{Datasets}
 
 \paragraph{Virtual KITTI}

thesis.tex

@@ -54,7 +54,7 @@
     url=false,
     hyperref=auto,
     giveninits=true,
-    maxbibnames=10
+    maxbibnames=100
 ]{biblatex}
 
 \renewbibmacro*{cite:seenote}{} % um zu verhindern, dass in Fußnoten automatisch "(wie Anm. xy)" eingefügt wird