Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Using Segmentation Masks in the ICCV 2019 Learning to Drive Challenge   In this work we predict vehicle speed and steering angle given camera image
frames. Our key contribution is using an external pre-trained neural network
for segmentation. We augment the raw images with their segmentation masks and
mirror images. We ensemble three diverse neural network models (i) a CNN using
a single image and its segmentation mask, (ii) a stacked CNN taking as input a
sequence of images and segmentation masks, and (iii) a bidirectional GRU,
extracting image features using a pre-trained ResNet34, DenseNet121 and our own
CNN single image model. We achieve the second best performance for MSE angle
and second best performance overall, to win 2nd place in the ICCV Learning to
Drive challenge. We make our models and code publicly available.

25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
33
The categories of this paper are: plasm-ph