| [39651] 無題 |
| 投稿者：diuwin |
投稿日：2024/09/22(Sun) 21:17:03 |  るるり14 |
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| Arianne ＞ Learning object-centric representations of advanced scenes is a promising step in direction of enabling efficient summary reasoning from low-stage perceptual features. Yet, most deep studying approaches study distributed representations that do not capture the compositional properties of pure scenes. In this paper, we present the Slot Attention module, an architectural part that interfaces with perceptual representations such because the output of a convolutional neural community and produces a set of task-dependent abstract representations which we call slots. These slots are exchangeable and might bind to any object within the input by specializing via a aggressive process over a number of rounds of attention. We empirically display that Slot Attention can extract object-centric representations that enable generalization to unseen compositions when trained on unsupervised object discovery and supervised property prediction duties. (2024/10/03(Thu) 22:53:36) どよの36 |
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