Object Pursuit: Building a Space of Objects via Discriminative Weight
Generation
- URL: http://arxiv.org/abs/2112.07954v1
- Date: Wed, 15 Dec 2021 08:25:30 GMT
- Title: Object Pursuit: Building a Space of Objects via Discriminative Weight
Generation
- Authors: Chuanyu Pan, Yanchao Yang, Kaichun Mo, Yueqi Duan, and Leonidas Guibas
- Abstract summary: We propose a framework to continuously learn object-centric representations for visual learning and understanding.
We leverage interactions to sample diverse variations of an object and the corresponding training signals while learning the object-centric representations.
We perform an extensive study of the key features of the proposed framework and analyze the characteristics of the learned representations.
- Score: 23.85039747700698
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We propose a framework to continuously learn object-centric representations
for visual learning and understanding. Existing object-centric representations
either rely on supervisions that individualize objects in the scene, or perform
unsupervised disentanglement that can hardly deal with complex scenes in the
real world. To mitigate the annotation burden and relax the constraints on the
statistical complexity of the data, our method leverages interactions to
effectively sample diverse variations of an object and the corresponding
training signals while learning the object-centric representations. Throughout
learning, objects are streamed one by one in random order with unknown
identities, and are associated with latent codes that can synthesize
discriminative weights for each object through a convolutional hypernetwork.
Moreover, re-identification of learned objects and forgetting prevention are
employed to make the learning process efficient and robust. We perform an
extensive study of the key features of the proposed framework and analyze the
characteristics of the learned representations. Furthermore, we demonstrate the
capability of the proposed framework in learning representations that can
improve label efficiency in downstream tasks. Our code and trained models will
be made publicly available.
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