We propose a data-driven approach that estimates the objectness of an image region by comparing it with a large database of exemplar object regions. Previous ...
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We propose a data-driven approach to estimate the likelihood that an image segment corresponds to a scene object (its "objectness") by comparing it to a ...
Oct 22, 2024 · We propose a data-driven approach to estimate the likelihood that an image segment corresponds to a scene object (its “objectness”) by ...
We propose a data-driven approach to estimate the likelihood that an image segment corresponds to a scene object (its “objectness”) by comparing it to a ...
We propose a data-driven approach to estimate the likelihood that an image segment corresponds to a scene object (its “objectness”) by comparing it to a ...
Jul 17, 2024 · The paper proposes a new testing approach for Deep Neural Networks (DNN) using gradient-free optimization to find perturbation chains that successfully falsify ...
Dec 15, 2024 · This paper presents DART, an innovative automated end-to-end pipeline that revolutionizes object detection workflows from data collection to model evaluation.
Dec 17, 2015 · This article deals with the detection of prominent objects in images. As opposed to the standard approaches based on sliding windows, ...
This thesis describes our three-pronged approach to solving this problem: (1) building simulation software to gather sufficient training data, (2) verifying and ...
This article deals with the detection of prominent objects in images. As opposed to the standard approaches based on sliding windows, we study a fundamentally ...
Missing: Objectness. | Show results with:Objectness.