WIP

abstract.tex

@@ -63,10 +63,10 @@ Bei Eingabe von zwei aufeinanderfolgenden frames aus einer monokularen RGB-D
 Kamera erkennt unser end-to-end Deep Network Objekte mit pixelgenauen Objektmasken
 und schätzt die 3D-Bewegung jedes erkannten Objekts zwischen den frames ab.
 Indem wir zusätzlich im selben Netzwerk die globale Kamerabewegung schätzen,
-setzen wir aus den instanzbasierten und globalen Bewegungsschätzungen dichten
-optischen Fluss zusammen.
+setzen wir aus den instanzbasierten und globalen Bewegungsschätzungen ein dichtes
+optisches Flussfeld zusammen.
 Wir trainieren unser Netzwerk auf dem synthetischen Virtual KITTI Datensatz,
-der ground truth für alle Komponenten unseres Systems bereitstellt.
+der Ground Truth für alle Komponenten unseres Systems bereitstellt.
 
 
 \end{abstract}

background.tex

@@ -205,7 +205,7 @@ Batch normalization \cite{BN} is used after every convolution.
 \caption{
 ResNet \cite{ResNet} \enquote{bottleneck} convolutional block introduced to reduce computational
 complexity in deeper network variants, shown here with 256 input and output channels.
-Figure from \cite{ResNet}.
+Figure taken from \cite{ResNet}.
 }
 \label{figure:bottleneck}
 \end{figure}
@@ -278,10 +278,10 @@ The basic Mask R-CNN ResNet-50 architecture is shown in Table \ref{table:maskrcn
 Note that the per-class masks logits are put through a sigmoid layer, and thus there is no
 comptetition between classes for the mask prediction branch.
 
-One important technical aspect of Mask R-CNN is the replacement of RoI pooling with
+One important additional technical aspect of Mask R-CNN is the replacement of RoI pooling with
 bilinear sampling for extracting the RoI features, which is much more precise.
-%In RoI pooling, at the borders, the bins for max-pooling are not aligned with the actual pixel
-%boundary of the bounding box.
+In the original RoI pooling from Fast R-CNN, the bins for max-pooling are not aligned with the actual pixel
+boundary of the bounding box, and thus some detail is lost.
 
 
 {
@@ -434,7 +434,7 @@ block (see Figure \ref{figure:fpn_block}).
 FPN block from \cite{FPN}.
 Lower resolution features coming from the bottleneck are bilinearly upsampled
 and added with higher resolution skip connections from the encoder.
-Figure from \cite{FPN}.
+Figure taken from \cite{FPN}.
 }
 \label{figure:fpn_block}
 \end{figure}

introduction.tex

@@ -48,9 +48,9 @@ often fails to properly segment the pixels into the correct masks or assigns bac
   \includegraphics[width=\textwidth]{figures/sfmnet_kitti}
 \caption{
 Results of SfM-Net \cite{SfmNet} on KITTI \cite{KITTI2015}.
-From left to right we show, instance segmentation into up to 3 independent objects,
+From left to right, we show their instance segmentation into up to 3 independent objects,
 ground truth instance masks for the segmented objects, composed optical flow and ground truth optical flow.
-Figure from \cite{SfmNet}.
+Figure taken from \cite{SfmNet}.
 }
 \label{figure:sfmnet_kitti}
 \end{figure}
@@ -67,7 +67,7 @@ and predicts pixel-precise segmentation masks for each detected object (Figure \
   \includegraphics[width=\textwidth]{figures/maskrcnn_cs}
 \caption{
 Instance segmentation results of Mask R-CNN ResNet-50-FPN \cite{MaskRCNN}
-on Cityscapes \cite{Cityscapes}. Figure from \cite{MaskRCNN}
+on Cityscapes \cite{Cityscapes}. Figure taken from \cite{MaskRCNN}.
 }
 \label{figure:maskrcnn_cs}
 \end{figure}