1,745 research outputs found
Reimagine BiSeNet for Real-Time Domain Adaptation in Semantic Segmentation
Semantic segmentation models have reached remarkable performance across
various tasks. However, this performance is achieved with extremely large
models, using powerful computational resources and without considering training
and inference time. Real-world applications, on the other hand, necessitate
models with minimal memory demands, efficient inference speed, and executable
with low-resources embedded devices, such as self-driving vehicles. In this
paper, we look at the challenge of real-time semantic segmentation across
domains, and we train a model to act appropriately on real-world data even
though it was trained on a synthetic realm. We employ a new lightweight and
shallow discriminator that was specifically created for this purpose. To the
best of our knowledge, we are the first to present a real-time adversarial
approach for assessing the domain adaption problem in semantic segmentation. We
tested our framework in the two standard protocol: GTA5 to Cityscapes and
SYNTHIA to Cityscapes. Code is available at:
https://github.com/taveraantonio/RTDA.Comment: Accepted at I-RIM 3D 202
EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition
Visual Place Recognition is a task that aims to predict the place of an image
(called query) based solely on its visual features. This is typically done
through image retrieval, where the query is matched to the most similar images
from a large database of geotagged photos, using learned global descriptors. A
major challenge in this task is recognizing places seen from different
viewpoints. To overcome this limitation, we propose a new method, called
EigenPlaces, to train our neural network on images from different point of
views, which embeds viewpoint robustness into the learned global descriptors.
The underlying idea is to cluster the training data so as to explicitly present
the model with different views of the same points of interest. The selection of
this points of interest is done without the need for extra supervision. We then
present experiments on the most comprehensive set of datasets in literature,
finding that EigenPlaces is able to outperform previous state of the art on the
majority of datasets, while requiring 60\% less GPU memory for training and
using 50\% smaller descriptors. The code and trained models for EigenPlaces are
available at {\small{\url{https://github.com/gmberton/EigenPlaces}}}, while
results with any other baseline can be computed with the codebase at
{\small{\url{https://github.com/gmberton/auto_VPR}}}.Comment: ICCV 202
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