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In this paper, we propose and study Open-World Tracking (OWT). Open-world tracking goes beyond current multi-object tracking benchmarks and methods which focus on tracking object classes that belong to a predefined closed-set of frequently observed object classes. In OWT, we relax this assumption: we may encounter objects at inference time that were not labeled for training. The main contribution of this paper is the formalization of the OWT task, along with an evaluation protocol and metric (Open-World Tracking Accuracy, OWTA), which decomposes into two intuitive terms, one for measuring recall, and another for measuring track association accuracy. This allows us to perform a rigorous evaluation of several different baselines that follow design patterns proposed in the multi-object tracking community. Further we show that our Open-World Tracking Baseline, while performing well in the OWT setting, also achieves near state-of-the-art results on traditional closed-world benchmarks, without any adjustments or tuning. We believe that this paper is an initial step towards studying multi-object tracking in the open world, a task of crucial importance for future intelligent agents that will need to understand, react to, and learn from, an infinite variety of objects that can appear in an open world.
We introduce a new image segmentation task, termed Entity Segmentation (ES) with the aim to segment all visual entities in an image without considering semantic category labels. It has many practical applications in image manipulation/editing where t
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As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from
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Recent years have witnessed the fast evolution and promising performance of the convolutional neural network (CNN)-based trackers, which aim at imitating biological visual systems. However, current CNN-based trackers can hardly generalize well to low