27,469 research outputs found
CASENet: Deep Category-Aware Semantic Edge Detection
Boundary and edge cues are highly beneficial in improving a wide variety of
vision tasks such as semantic segmentation, object recognition, stereo, and
object proposal generation. Recently, the problem of edge detection has been
revisited and significant progress has been made with deep learning. While
classical edge detection is a challenging binary problem in itself, the
category-aware semantic edge detection by nature is an even more challenging
multi-label problem. We model the problem such that each edge pixel can be
associated with more than one class as they appear in contours or junctions
belonging to two or more semantic classes. To this end, we propose a novel
end-to-end deep semantic edge learning architecture based on ResNet and a new
skip-layer architecture where category-wise edge activations at the top
convolution layer share and are fused with the same set of bottom layer
features. We then propose a multi-label loss function to supervise the fused
activations. We show that our proposed architecture benefits this problem with
better performance, and we outperform the current state-of-the-art semantic
edge detection methods by a large margin on standard data sets such as SBD and
Cityscapes.Comment: Accepted to CVPR 201
Grounding semantics in robots for Visual Question Answering
In this thesis I describe an operational implementation of an object detection and description system that incorporates in an end-to-end Visual Question Answering system and evaluated it on two visual question answering datasets for compositional language and elementary visual reasoning
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