Cognition refers to advanced pattern recognition capabilities in models and  systems. Machine learning algorithms, particularly those based on neural networks, can learn to recognize patterns in data and make predictions or classifications. Cognition is evident in machine learning applications related to computer vision, where algorithms can interpret visual data, recognize objects, and understand the context of images or videos.

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Peek-a-boo: Occlusion Reasoning in Indoor Scenes with Plane Representations

Peek-a-boo: Occlusion Reasoning in Indoor Scenes with Plane Representations We address the challenging task of occlusion-aware indoor 3D scene understanding. We represent scenes by a set of planes, where each one is defined by its normal, offset and two masks outlining (i) the extent of the visible part and (ii) the full region that consists of both visible and occluded parts of the plane. We infer these planes from a single input image with a novel neural network architecture. It consists of a two-branch category-specific module that aims to predict layout and objects of the scene separately so that different types of planes can be handled better. We also introduce a novel loss function based on plane warping that can leverage multiple views at training time for improved occlusion-aware reasoning. In order to train and evaluate our occlusion-reasoning model, we use the ScanNet dataset and propose (i) a strategy to automatically extract ground truth for both visible and hidden regions and (ii) a new evaluation metric that specifically focuses on the prediction in hidden regions. We empirically demonstrate that our proposed approach can achieve higher accuracy for occlusion reasoning compared to competitive baselines on the ScanNet dataset, e.g. 42.65% relative improvement on hidden regions.