Zobrazeno 51 - 60
of 510
pro vyhledávání: '"Barnes, Nick"'
Deep convolutional neural networks perform better on images containing spatially invariant degradations, also known as synthetic degradations; however, their performance is limited on real-degraded photographs and requires multiple-stage network mode
Externí odkaz:
http://arxiv.org/abs/2004.13524
Autor:
Zhang, Jing, Fan, Deng-Ping, Dai, Yuchao, Anwar, Saeed, Saleh, Fatemeh Sadat, Zhang, Tong, Barnes, Nick
In this paper, we propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Existing RGB-D saliency detection methods treat the saliency detection task as a point estimation pro
Externí odkaz:
http://arxiv.org/abs/2004.05763
To make the best use of the underlying minute and subtle differences, fine-grained classifiers collect information about inter-class variations. The task is very challenging due to the small differences between the colors, viewpoint, and structure in
Externí odkaz:
http://arxiv.org/abs/2003.11154
Autor:
Stoffregen, Timo, Scheerlinck, Cedric, Scaramuzza, Davide, Drummond, Tom, Barnes, Nick, Kleeman, Lindsay, Mahony, Robert
Publikováno v:
European Conference on Computer Vision, 2020
Event cameras are paradigm-shifting novel sensors that report asynchronous, per-pixel brightness changes called 'events' with unparalleled low latency. This makes them ideal for high speed, high dynamic range scenes where conventional cameras would f
Externí odkaz:
http://arxiv.org/abs/2003.09078
Previous work on novel object detection considers zero or few-shot settings where none or few examples of each category are available for training. In real world scenarios, it is less practical to expect that 'all' the novel classes are either unseen
Externí odkaz:
http://arxiv.org/abs/2003.07003
Visual Question Answering (VQA) has emerged as a Visual Turing Test to validate the reasoning ability of AI agents. The pivot to existing VQA models is the joint embedding that is learned by combining the visual features from an image and the semanti
Externí odkaz:
http://arxiv.org/abs/2001.07059
Point-clouds are a popular choice for vision and graphics tasks due to their accurate shape description and direct acquisition from range-scanners. This demands the ability to synthesize and reconstruct high-quality point-clouds. Current deep generat
Externí odkaz:
http://arxiv.org/abs/1912.01800
Convolution is an integral operation that defines how the shape of one function is modified by another function. This powerful concept forms the basis of hierarchical feature learning in deep neural networks. Although performing convolution in Euclid
Externí odkaz:
http://arxiv.org/abs/1912.01454
As the basic task of point cloud analysis, classification is fundamental but always challenging. To address some unsolved problems of existing methods, we propose a network that captures geometric features of point clouds for better representations.
Externí odkaz:
http://arxiv.org/abs/1911.12885
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