Compressive Self-localization Using Relative Attribute Embedding
This work addresses the problem of robust and efficient place recognition for robotics and autonomous systems, but it appears incremental as it builds on existing attribute-based approaches.
The paper tackled visual place recognition by exploring relative attribute-based image embeddings as a domain-adaptive compact descriptor, orthogonal to absolute attribute-based methods, and reported results showing improved performance in terms of accuracy and efficiency.
The use of relative attribute (e.g., beautiful, safe, convenient) -based image embeddings in visual place recognition, as a domain-adaptive compact image descriptor that is orthogonal to the typical approach of absolute attribute (e.g., color, shape, texture) -based image embeddings, is explored in this paper.