1,622 research outputs found
Structured Assessment on Learning Progress
This paper presented a novel technique and practice of the assessment of learning progress of university students in an engineering discipline. Instead of measuring the effectiveness of accumulation of specific knowledge, the newly developed assessment technique evaluates the development of the intelligence of the students. The key components of the proposed technique are a performance-based method for the estimation of the intelligence level and a cognitive mental faculty-oriented decomposition method to determine the intelligence contribution factors for learning subjects and exam questions. The proposed technique was applied to assess the learning progress of a group of university students in the field of automation, and the results from test agreed with the expectation well
Cascade Residual Learning: A Two-stage Convolutional Neural Network for Stereo Matching
Leveraging on the recent developments in convolutional neural networks
(CNNs), matching dense correspondence from a stereo pair has been cast as a
learning problem, with performance exceeding traditional approaches. However,
it remains challenging to generate high-quality disparities for the inherently
ill-posed regions. To tackle this problem, we propose a novel cascade CNN
architecture composing of two stages. The first stage advances the recently
proposed DispNet by equipping it with extra up-convolution modules, leading to
disparity images with more details. The second stage explicitly rectifies the
disparity initialized by the first stage; it couples with the first-stage and
generates residual signals across multiple scales. The summation of the outputs
from the two stages gives the final disparity. As opposed to directly learning
the disparity at the second stage, we show that residual learning provides more
effective refinement. Moreover, it also benefits the training of the overall
cascade network. Experimentation shows that our cascade residual learning
scheme provides state-of-the-art performance for matching stereo
correspondence. By the time of the submission of this paper, our method ranks
first in the KITTI 2015 stereo benchmark, surpassing the prior works by a
noteworthy margin.Comment: Accepted at ICCVW 2017. The first two authors contributed equally to
this pape
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