Enhancing Landmark Detection in Cluttered Real-World Scenarios with
  Vision Transformers
Mohammad Javad Rajabi Morteza Mirzai Ahmad Nickabadi
Visual place recognition tasks often encounter significant challenges in
landmark detection due to the presence of irrelevant objects such as humans,
cars, and trees, despite the remarkable progress achieved by previous models,
especially in the context of transformers. To address this issue, we propose a
novel method that effectively leverages the strengths of vision transformers.
By employing a meticulous selection process, our approach identifies and
isolates specific patches within the image that correspond to occluding
objects. To evaluate the efficacy of our method, we created augmented datasets
and conducted comprehensive testing. The results demonstrate the superior
accuracy achieved by our proposed approach. This research contributes to the
advancement of landmark detection in visual place recognition and shows the
potential of leveraging vision transformers to overcome challenges posed by
cluttered real-world scenarios.