Exploiting Scene-specific Features for Object Goal Navigation

By Tommaso Campari, Paolo Eccher, Luciano Serafini, Lamberto Ballan. 2020

41 citations recorded by the source corpus.

Can the intrinsic relation between an object and the room in which it is usually located help agents in the Visual Navigation Task? We study this question in the context of Object Navigation, a problem in which an agent has to reach an object of a specific class while moving in a complex domestic environment. In this paper, we introduce a new reduced dataset that speeds up the training of navigation models, a notoriously complex task. Our proposed dataset permits the training of models that do not exploit online-built maps in reasonable times even without the use of huge computational resources. Therefore, this reduced dataset guarantees a significant benchmark and it can be used to identify promising models that could be then tried on bigger and more challenging datasets. Subsequently, we propose the SMTSC model, an attention-based model capable of exploiting the correlation between scenes and objects contained in them, highlighting quantitatively how the idea is correct.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Jiafei Duan
2. Samson Yu
3. Cheston Tan
4. Tangyao Li
5. Huaiyu Zhu

Citations in the corpus

• A Survey of Embodied AI: From Simulators to Research Tasks (2021, background): “Under point navigation [80], the agent is tasked to navigate to a specific point while in object navigation [81, 38], the agent is tasked to navigate to an object of a specific class.”