WIP

background.tex

@@ -1,4 +1,4 @@
-Here, we will give a more detailed description of previous works
+In this section, we will give a more detailed description of previous works
 we directly build on and other prerequisites.
 
 \subsection{Optical flow and scene flow}
@@ -50,16 +50,17 @@ most popular deep networks for object detection, and have recently also been app
 \paragraph{R-CNN}
 Region-based convolutional networks (R-CNNs) \cite{RCNN} use a non-learned algorithm external to a standard encoder CNN
 for computing \emph{region proposals} in the shape of 2D bounding boxes, which represent regions that may contain an object.
-For each of the region proposals, the input image is cropped at the proposed region and the crop is
+For each of the region proposals, the input image is cropped using the regions bounding box and the crop is
 passed through a CNN, which performs classification of the object (or non-object, if the region shows background). % and box refinement!
 
 \paragraph{Fast R-CNN}
 The original R-CNN involves computing one forward pass of the CNN for each of the region proposals,
-which is costly, as there is generally a large amount of proposals.
+which is costly, as there is generally a large number of proposals.
 Fast R-CNN \cite{FastRCNN} significantly reduces computation by performing only a single forward pass with the whole image
 as input to the CNN (compared to the sequential input of crops in the case of R-CNN).
 Then, fixed size crops are taken from the compressed feature map of the image,
-collected into a batch and passed into a small Fast R-CNN
+each corresponding to one of the proposal bounding boxes.
+The crops are collected into a batch and passed into a small Fast R-CNN
 \emph{head} network, which performs classification and prediction of refined boxes for all regions in one forward pass.
 This technique is called \emph{RoI pooling}. % TODO explain how RoI pooling converts full image box coords to crop ranges
 Thus, given region proposals, the per-region computation is reduced to a single pass through the complete network,
@@ -75,7 +76,7 @@ and again, improved accuracy.
 This unified network operates in two stages.
 In the \emph{first stage}, one forward pass is performed on the \emph{backbone} network,
 which is a deep feature encoder CNN with the original image as input.
-Next, the \emph{backbone} features are passed into a small, fully convolutional \emph{Region Proposal Network (RPN)} head, which
+Next, the \emph{backbone} output features are passed into a small, fully convolutional \emph{Region Proposal Network (RPN)} head, which
 predicts objectness scores and regresses bounding boxes at each of its output positions.
 At any position, bounding boxes are predicted as offsets relative to a fixed set of \emph{anchors} with different
 aspect ratios.
@@ -84,10 +85,9 @@ For each anchor at a given position, the objectness score tells us how likely th
 The region proposals can then be obtained as the N highest scoring anchor boxes.
 
 The \emph{second stage} corresponds to the original Fast R-CNN head network, performing classification
-and bounding box refinement for each region proposal.
+and bounding box refinement for each region proposal. % TODO verify that it isn't modified
 As in Fast R-CNN, RoI pooling is used to crop one fixed size feature map for each of the region proposals.
 
-
 \paragraph{Mask R-CNN}
 Faster R-CNN and the earlier systems detect and classify objects at bounding box granularity.
 However, it can be helpful to know class and object (instance) membership of all individual pixels,
@@ -101,5 +101,10 @@ In addition to extending the original Faster R-CNN head, Mask R-CNN also introdu
 variant based on Feature Pyramid Networks \cite{FPN}.
 Figure \ref{} compares the two Mask R-CNN head variants.
 
+\paragraph{Feature Pyramid Networks}
+\todo{TODO}
+
 \paragraph{Supervision of the RPN}
+\todo{TODO}
 \paragraph{Supervision of the RoI head}
+\todo{TODO}

bib.bib

@@ -182,7 +182,7 @@
 @inproceedings{CensusTerm,
   author = {Fridtjof Stein},
   title = {Efficient Computation of Optical Flow Using the Census Transform},
-  booktitle = {DAGM},
+  booktitle = {{DAGM} Symposium},
   year = {2004}}
 
 @inproceedings{DeeperDepth,

conclusion.tex

@@ -1,3 +1,4 @@
+\subsection{Summary}
 We have introduced an extension on top of region-based convolutional networks to enable object motion estimation
 in parallel to instance segmentation.
 \todo{complete}

introduction.tex

@@ -10,9 +10,8 @@ if technically feasible, as camera sensors are cheap and ubiquitous.
 For example, in autonomous driving, it is crucial to not only know the position
 of each obstacle, but to also know if and where the obstacle is moving,
 and to use sensors that will not make the system too expensive for widespread use.
-There are many other applications.. %TODO(make motivation wider)
 
-A promising approach for 3D scene understanding in these situations are deep neural
+A promising approach for 3D scene understanding in situations like these are deep neural
 networks, which have recently achieved breakthroughs in object detection, instance segmentation and classification
 in still images and are more and more often being applied to video data.
 A key benefit of end-to-end deep networks is that they can, in principle,