Browsing Publications by Author "Sasaki, Tomotake"
Now showing items 1-4 of 4
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Do Neural Networks for Segmentation Understand Insideness?
(Center for Brains, Minds and Machines (CBMM), 2020-04-04)The insideness problem is an image segmentation modality that consists of determining which pixels are inside and outside a region. Deep Neural Networks (DNNs) excel in segmentation benchmarks, but it is unclear that they ... -
On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations
(Center for Brains, Minds and Machines (CBMM), 2020-07-17)Recognizing an object’s category and pose lies at the heart of visual understanding. Recent works suggest that deep neural networks (DNNs) often fail to generalize to category-pose combinations not seen during training. ... -
Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations
(Center for Brains, Minds and Machines (CBMM), 2022-01-26)The training data distribution is often biased towards objects in certain orientations and illumination conditions. While humans have a remarkable capability of recognizing objects in out-of-distribution (OoD) orientations ... -
Transformer Module Networks for Systematic Generalization in Visual Question Answering
(Center for Brains, Minds and Machines (CBMM), 2022-02-03)Transformer-based models achieve great performance on Visual Question Answering (VQA). How- ever, when we evaluate them on systematic generalization, i.e., handling novel combinations of known concepts, their performance ...
