Search Results for author: Kento Ohtani

Found 4 papers, 3 papers with code

DRUformer: Enhancing the driving scene Important object detection with driving relationship self-understanding

no code implementations11 Nov 2023 Yingjie Niu, Ming Ding, Keisuke Fujii, Kento Ohtani, Alexander Carballo, Kazuya Takeda

The DRUformer is a transformer-based multi-modal important object detection model that takes into account the relationships between all the participants in the driving scenario.

object-detection Object Detection

Predictive World Models from Real-World Partial Observations

1 code implementation12 Jan 2023 Robin Karlsson, Alexander Carballo, Keisuke Fujii, Kento Ohtani, Kazuya Takeda

By extending HVAEs to cases where complete ground truth states do not exist, we facilitate continual learning of spatial prediction as a step towards realizing explainable and comprehensive predictive world models for real-world mobile robotics applications.

Continual Learning Open-Ended Question Answering +1

ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment

1 code implementation24 Nov 2021 Robin Karlsson, Tomoki Hayashi, Keisuke Fujii, Alexander Carballo, Kento Ohtani, Kazuya Takeda

Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data.

Contrastive Learning Domain Generalization +4

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