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✓ ✓ ✓ 0.493 0.591 12 0.390 0.482 6 |
Table 1: Cross-view retrieval performance of Sat2Cap model: We experiment with three different settings in our model. |
First, we experiment with the effect of using the Dynamic Encoder. Secondly, we look at the performance degradation in |
scenarios where meta-information is not available in inference. Finally, we experiment with randomly dropping out the |
Dynamic Encoder during training. |
to the model. We encode this meta-information using sin- |
cos encoding. We use a very shallow fully connected layer |
which we call the Dynamic Encoder represented by hθ. The |
encoded meta information is passed through the Dynamic |
Encoder whose output is added element-wise to the unnor- |
malized output from the Sat2Cap model. We then normal- |
ize the final sum to compute our objective. Our framework, |
shown in Figure 2, is defined as: |
Oi=gθ(oi) (4)Ei=hθ(ei) (5) |
where eiis the output of sin-cos encoding of the date, time, |
and location information for sample i. |
Si=Oi+Ei (6) |
Now the objective function is updated to: |
Ldynamic =1 |
kk∑ |
i=0−logexp(Si·Gi/τ) |
∑k |
j=0exp(Si·Gj/τ)(7) |
4 |
Figure 3: Top-9 overhead-to-ground image retrieval: We use the Sat2Cap embeddings of the overhead images and CLIP |
embeddings of the ground-level images and show the 9 closest ground-level images retrieved for a query overhead image. |
The retrieval was performed using 10,000 samples. |
Our training dataset captures the ground-level scenes at |
various times. If the model is only allowed to learn using the |
overhead image of a location, it will be forced to learn an av- |
erage concept for all temporal settings. By conditioning the |
problem on additional temporal data, our model learns dif- |
ferent ground-level concepts for different times of the day |
and year. This ultimately allows Sat2Cap to dynamically |
adapt to temporal variations for the same geolocation. |
To prevent overfitting to the meta-information, we imple- |
ment random dropout of the Dynamic Encoder during train- |
ing. This improves retrieval performance but can decrease |
the model’s sensitivity to temporal variations. The dropout |
causes the model to learn to disregard the meta-information |
as it is frequently dropped out during training. Therefore, |
we view dropout as a hyperparameter that can be adjusted |
to control the dynamic sensitivity of our model. |
3.4. Implementations Details |
We use a ViT-32B as the CLIP image encoder. This im- |
age encoder is kept frozen throughout our training proce- |
dure. We use a ViT-32B architecture as the backbone for |
our Sat2Cap model. The Sat2Cap backbone is initialized |
using CLIP weights. Following [7] we use an AdamW opti- |
mizer [37] with a learning rate of 1e−05withβ1= 0.9and |
β2= 0.98. We also use a learnable temperature parameterwhich is initialized at τ= 0.07. We use Cosine Annealing |
with Warm Restarts [38] as the learning rate scheduler. |
We augment the overhead images using RandomRe- |
sizedCrop and RandAugment [39]. The overhead images |
are normalized using the mean and standard deviation of |
the dataset. The training was carried out using Nvidia A100 |
40GB GPU. Since a larger number of negative samples is |
beneficial for contrastive learning, we simulate a large batch |
size using a memory bank approach. We initialize a queue |
of size 9600 and fill it with precomputed ground-level im- |
age CLIP embeddings which are used as negative samples |
for computing the loss. |
4. Experiments and Results |
Our model learns a powerful geo-text embedding space |
that can be used for a variety of applications. We experi- |
mented with four tasks and show the quantitative and qual- |
itative results on them. |
4.1. Application 1: Cross-View Image Retrieval |
In this experiment, we show that our model learns a |
strong relationship between co-located overhead images |
and ground-level images in the CLIP space. We randomly |
sample 10,000 image pairs from the test set for this exper- |
iment. First, we compute the Sat2Cap embeddings for all |
5 |
Figure 4: Top-9 overhead-to-ground image retrieval with temporal manipulation: We show the 9 closest ground-level |
images for a query overhead image at two different time settings (11:00 p.m. and 08:00 a.m.). |
overhead images in the 10k testset. Then we compute the |
CLIP embeddings for all ground-level images in the set. We |
then compute top-k and median rank metrics between the |
Sat2Cap overhead embeddings and the CLIP ground em- |
beddings. Table 1 shows all retrieval results. |
As a baseline, we use the distance between CLIP over- |
head embeddings and CLIP ground embeddings. We get |
an extremely low R@10 score of 0.013 and a median rank |
of 1700. These low scores essentially tell us that the over- |
head images and corresponding ground-level images lie far |
apart in the CLIP space. For Sat2Cap, we first experiment |
without using the Dynamic Encoder. Just by contrastively |
training the Sat2Cap image encoder with ground-level CLIP |
embeddings, we achieve a high R@10 score of 0.493 and a |
median rank of 15. |
All remaining experiments are conducted on models that |
were trained using the Dynamic Encoder. Table 1 shows |
that initially, the retrieval scores drop when using the Dy- |
namic Encoder. We suspect this happens because the model |
starts to overfit on the meta-information, ignoring important |
cues from the overhead images. We also see a 5.4% drop in |
R@10 metrics when we remove the meta-information dur-ing inference. To reduce the possibility of overfitting, we |
randomly drop the Dynamic Encoder during training. We |
see that simply adding dropout during training increases |
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