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Did it change? Learning to detect point-of-interest changes for proactive map updates
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| Jérome Revaud, Rafael Sampaio De Rezende, Minhyeok Heo, Chanmi You, Seong-Gyun Jeong |
| Multi-modal Learning and Application Workshop (CVPR), Long Beach, USA, 16-20 June, 2019 |
Maps are an increasingly important tool in our daily lives, yet their rich semantic content still largely depends on manual input. Motivated by the broad availability of geo-tagged street-view images, we propose a new task aiming to make the map update process more proactive. We focus on automatically detecting changes of Points of Interest (POIs), specifically stores or shops of any kind, based on visual input. Faced with the lack of an appropriate benchmark, we build and release a large dataset, captured in two large shopping centers, that comprises 33K geo-localized images and 578 POIs. We then design a generic approach that compares two image sets captured in the same venue at different times and outputs POI changes as a ranked list of map locations. In contrast to logo or franchise recognition approaches, our system does not depend on an external franchise database. It is instead inspired by recent deep metric learning approaches that learn a similarity function fit to the task at hand. We compare various loss functions to learn a metric aligned with the POI change detection goal, and report promising results.
© IEEE
More details about this paper and the Mallscape dataset can be found in the related blog post.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
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The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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