Datasets:
gremlin97
/
RemoteSensingCorpus

Dataset card Data Studio Files
xet
 Community
1
text
stringlengths
0
820
and answers is more limited than what it is in traditional VQA
datasets (9 possible answers for the LR dataset, 98 for the HRdataset).
III. VQA M ODEL
We investigate the difficulty of the VQA task for remote
sensing using a basic VQA model based on deep learning. An
illustration of the proposed network is shown in Figure 7. In
their simple form, VQA models are composed of three parts
[24]:
A. feature extraction;
B. fusion of these features to obtain a single feature vector
representing both the visual information and the question;
C. prediction based on this vector.
As the model shown in Figure 7 is learned end-to-end, the
vector obtained after the fusion (in green in Figure 7) can be
seen as a joint embedding of both the image and the question
which is used as an input for the prediction step. We detail
each of these 3 parts in the following.
A. Feature extraction
The first component of our VQA model is the feature extrac-
tion. Its purpose is to obtain a low-dimensional representation
of the information contained in the image and the question.
1) Visual part: To extract information from a 2D image,
a common choice is to use a Convolutional Neural Network
(CNN). Specifically, we use a Resnet-152 model [32] pre-
trained on ImageNet [10]. The principal motivation for this
choice is that this architecture manages to avoid the un-
desirable degradation problem (decreasing performance with
deeper networks) by using residual mappings of the layers’
inputs which are easier to learn than the common choice of
direct mappings. This architecture has been succesfully used
in a wide range of work in the remote sensing community
(e.g. [8], [17], [33]). The last average pooling layer and fully
connected layer are replaced by a 1×12D convolution which
outputs a total of 2048 features which are vectorized. A final
PRE-PRINT. FINAL VERSION IN IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 7
⊙B. Fusion A. Features extraction
CNN
ResNet-152
"Is there a building?" RNN
skip-thoughts
2048 1200
1200 2400
1200 256Output
vector
Yes
No
89 ...
A. 1) Visual part
A. 2) Language partC. Prediction
⊙
Fully connected layer
Point wise multiplication
Legend
Fig. 7. Framework of the proposed Visual Question Answering model.
fully connected layer is learned to obtain a 1200 dimension
vector.
2) Language part: The feature vector is obtained using the
skip-thoughts model [34] trained on the BookCorpus dataset
[35]. This model is a recurrent neural network, which aims at
producing a vector representing a sequence of words (in our
case, a question). To make this vector informative, the model
is trained in the following way: it encodes a sentence from a
book in a latent space, and tries to decode it to obtain the two
adjacent sentences in the book. By doing so, it ensures that
the latent space embeds semantic information. Note that this
semantic information is not remote sensing specific due to the
BookCorpus dataset it has been trained on. However, several
works, including [36], have successfully applied non-domain
specific NLP models to remote sensing. In our model, we use
the encoder which is then followed by a fully-connected layer
(from size 2400 elements to 1200).
B. Fusion
At this step, we have two feature vectors (one representing
the image, one representing the question) of the same size. To
merge them into a single vector, we use a simple strategy: a
point-wise multiplication after applying the hyperbolic tangent
function to the vectors’ elements. While being a fixed (i.e.
not learnt) operation, the end-to-end training of our model
encourages both feature vectors to be comparable with respect
to this operation.
C. Prediction
Finally, we project this 1200 dimensional vector to the
answer space by using a MLP with one hidden layer of 256
elements. We formulate the problem as a classification task, in
which each possible answer is a class. Therefore, the size of
the output vector depends on the number of possible answers.D. Training procedure
We train the model using the Adam optimizer [37] with a
learning rate of 10−5until convergence (150 epochs in the
case of the LR dataset, and 35 epochs in the case of the HR
dataset). We use a dropout of 0.5 for every fully connected
layer. Due to the difference of input size between the two
datasets (HR images are 4 times larger), we use batches of
70 instances for the HR dataset and 280 for the LR dataset.
Furthermore, when the questions do not contain a positional
component relative to the image space (i.e. ”left of”, ”top of”,
”right of” or ”bottom of”, see subsection II-A), we augment the
image space by randomly applying vertical and/or horizontal
flipping
IV. R ESULTS AND DISCUSSION
We report the results obtained by our model on the test sets