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12,205 results on '"Zhu, Qing"'

157. Semi-Supervised Adversarial Recognition of Refined Window Structures for Inverse Procedural Fa\c{c}ade Modeling

Author

Hu, Han, Liang, Xinrong, Ding, Yulin, Shang, Qisen, Xu, Bo, Ge, Xuming, Chen, Min, Zhong, Ruofei, and Zhu, Qing

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep learning methods are notoriously data-hungry, which requires a large number of labeled samples. Unfortunately, the large amount of interactive sample labeling efforts has dramatically hindered the application of deep learning methods, especially for 3D modeling tasks, which require heterogeneous samples. To alleviate the work of data annotation for learned 3D modeling of fa\c{c}ades, this paper proposed a semi-supervised adversarial recognition strategy embedded in inverse procedural modeling. Beginning with textured LOD-2 (Level-of-Details) models, we use the classical convolutional neural networks to recognize the types and estimate the parameters of windows from image patches. The window types and parameters are then assembled into procedural grammar. A simple procedural engine is built inside an existing 3D modeling software, producing fine-grained window geometries. To obtain a useful model from a few labeled samples, we leverage the generative adversarial network to train the feature extractor in a semi-supervised manner. The adversarial training strategy can also exploit unlabeled data to make the training phase more stable. Experiments using publicly available fa\c{c}ade image datasets reveal that the proposed training strategy can obtain about 10% improvement in classification accuracy and 50% improvement in parameter estimation under the same network structure. In addition, performance gains are more pronounced when testing against unseen data featuring different fa\c{c}ade styles.

Published
2022

175. Reviewing continual learning from the perspective of human-level intelligence

Author

Chang, Yifan, Li, Wenbo, Peng, Jian, Tang, Bo, Kang, Yu, Lei, Yinjie, Gui, Yuanmiao, Zhu, Qing, Liu, Yu, and Li, Haifeng

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Neural and Evolutionary Computing
Abstract

Humans' continual learning (CL) ability is closely related to Stability Versus Plasticity Dilemma that describes how humans achieve ongoing learning capacity and preservation for learned information. The notion of CL has always been present in artificial intelligence (AI) since its births. This paper proposes a comprehensive review of CL. Different from previous reviews that mainly focus on the catastrophic forgetting phenomenon in CL, this paper surveys CL from a more macroscopic perspective based on the Stability Versus Plasticity mechanism. Analogous to biological counterpart, "smart" AI agents are supposed to i) remember previously learned information (information retrospection); ii) infer on new information continuously (information prospection:); iii) transfer useful information (information transfer), to achieve high-level CL. According to the taxonomy, evaluation metrics, algorithms, applications as well as some open issues are then introduced. Our main contributions concern i) rechecking CL from the level of artificial general intelligence; ii) providing a detailed and extensive overview on CL topics; iii) presenting some novel ideas on the potential development of CL., Comment: 21 pages, 10 figures

Published
2021

176. Meta-learning an Intermediate Representation for Few-shot Block-wise Prediction of Landslide Susceptibility

Author

Chen, Li, Ding, Yulin, Pirasteh, Saeid, Hu, Han, Zhu, Qing, Zeng, Haowei, Yu, Haojia, Shang, Qisen, and Song, Yongfei

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Predicting a landslide susceptibility map (LSM) is essential for risk recognition and disaster prevention. Despite the successful application of data-driven approaches for LSM prediction, most methods generally apply a single global model to predict the LSM for an entire target region. However, in large-scale areas with significant environmental change, various parts of the region hold different landslide-inducing environments, and therefore, should be predicted with respective models. This study first segmented target scenarios into blocks for individual analysis. Then, the critical problem is that in each block with limited samples, conducting training and testing a model is impossible for a satisfactory LSM prediction, especially in dangerous mountainous areas where landslide surveying is expensive. To solve the problem, we trained an intermediate representation by the meta-learning paradigm, which is superior for capturing information valuable for few-shot adaption from LSM tasks. We hypothesized that there are more general and vital concepts concerning landslide causes and are sensitive to variations in input features. Thus, we can quickly few-shot adapt the models from the intermediate representation for different blocks or even unseen tasks using very few exemplar samples. Experimental results on the two study areas demonstrated the validity of our block-wise analysis in large scenarios and revealed the top few-shot adaption performances of the proposed methods.

Published
2021
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