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