no code implementations • 28 Aug 2023 • Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura
To address these challenges, we propose a saliency-guided approach that leverages attention information to improve the performance of LiDAR odometry estimation and semantic segmentation models.
no code implementations • 28 Jan 2022 • Ali Caglayan, Nevrez Imamoglu, Oguzhan Guclu, Ali Osman Serhatoglu, Weimin WANG, Ahmet Burak Can, Ryosuke Nakamura
This can be very useful for visual tasks such as simultaneous localization and mapping (SLAM) where CNN representations of spatially attentive object locations may lead to improved performance.
1 code implementation • 7 Dec 2021 • Guanqun Ding, Nevrez Imamoglu, Ali Caglayan, Masahiro Murakawa, Ryosuke Nakamura
We first use the proposed feedback model to learn saliency distribution from pseudo-ground-truth.
1 code implementation • 26 Apr 2020 • Ali Caglayan, Nevrez Imamoglu, Ahmet Burak Can, Ryosuke Nakamura
The second stage maps these features into high level representations with a fully randomized structure of recursive neural networks (RNNs) efficiently.
Ranked #2 on Scene Recognition on SUN-RGBD