slot abstract - SlotTTA a semisupervised slotcentric scene decomposition culture artinya model that at test time is adapted per scene through gradient descent on reconstruction or crossview synthesis objectives Testtime Adaptation with SlotCentric Models Mihir Abstract Current visual detectors though impressive within their training distribution often fail to Recent work has demonstrated strong systematic generalization in visual reasoning tasks involving multiobject inputs through the integration of slotbased methods used for extracting objectcentric representations coupled with strong inductive biases for relational abstraction Learning objectcentric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from lowlevel perceptual features Yet most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes In this paper we present the Slot Attention Abstract Objectcentric learning OCL extracts the representation of objects with slots offering an exceptional blend of flexibility and interpretability for abstracting lowlevel perceptual features A widely adopted method within OCL is slot attention which utilizes attention mechanisms to iteratively refine slot representations However a Slot Abstractors Toward Scalable Abstract Visual Reasoning Slot Abstractors is proposed an approach to abstract visual reasoning that can be scaled to problems involving a large number of objects and multiple relations among them and displays stateoftheart performance across four abstract visual reasoning tasks as well as an abstract reasoning task involving realworld images Abstract visual reasoning is a characteristically human ability Slot Abstractors Toward Scalable Abstract Visual Reasoning arXivorg In this paper we present the Slot Attention module an architectural component that interfaces with perceptual representations margo 123 slot such as the output of a convolutional neural network and produces a set of taskdependent abstract representations which we call slots Recent work has demonstrated strong systematic generalization in visual reasoning tasks involving multiobject inputs through the integration of slotbased methods used for extracting objectcentric representations coupled with strong inductive biases for relational abstraction 200615055 ObjectCentric Learning with Slot Attention arXivorg CVPR 2024 Open Access Repository ObjectCentric Learning with Slot Attention Papers With Code Learning objectcentric representations of complex scenes is a promising step towards enabling efficient abstract reasoning from lowlevel perceptual features Yet most deep learning approaches learn distributed representations that do not capture the compositional properties of natural scenes In this paper we present the Slot Attention module an architectural component that interfaces Slot Abstractors Toward Scalable Abstract Visual Reasoning We evaluate the Slot Abstractor on five abstract visual reasoning tasks including a task involving realworld images with a diverse range of visual and rule complexity We find that the Slot Abstractor is capable of strong systematic generalization of learned abstract rules and can be scaled to problems with multiple rule types and more Abstract Automatically discovering composable abstractions from raw perceptual data is a longstanding challenge in machine learning Recent slotbased neural networks that learn about objects in a selfsupervised manner have made exciting progress in this direction However they typically fall short at adequately capturing spatial symmetries Objectcentric learning with slot attention Proceedings of the 34th Slot Abstractors Toward Scalable Abstract Visual Reasoning sakura slot SlotTTA Invariant Slot Attention
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