Keyword-guided neural conversational model by Zhong, Peixiang, Liu, Yong, Wang, Hao, Miao, Chunyan

“…In this paper, we assume that human conversations are grounded on commonsense and propose a keyword-guided neural conversational model that can leverage external commonsense knowledge graphs (CKG) for both keyword transition and response retrieval. …”
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Neuron coverage-guided domain generalization by Tian, Chris Xing, Li, Haoliang, Xie, Xiaofei, Liu, Yang, Wang, Shiqi

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Seed-guided topic model for document filtering and classification by Li, Chenliang, Chen, Shiqian, Xing, Jian, Sun, Aixin, Ma, Zongyang

“…In this article, we propose a seed-guided topic model for the dataless text filtering and classification (named DFC). …”
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Progressive self-guided loss for salient object detection by Yang, Sheng, Lin, Weisi, Lin, Guosheng, Jiang, Qiuping, Liu, Zichuan

“…We present a simple yet effective progressive self-guided loss function to facilitate deep learning-based salient object detection (SOD) in images. …”
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Aspect-guided syntax graph learning for explainable recommendation by Hu, Yidan, Liu, Yong, Miao, Chunyan, Lin, Gongqi, Miao, Yuan

“…Then, an aspect-guided explanation decoder is developed to generate aspects and aspect-relevant explanations based on the attention mechanism. …”
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Threshold-guided design and optimization for Harris corner detector architecture by Jasani, Bhavan Ashwin, Lam, Siew-Kei, Meher, Pramod Kumar, Wu, Meiqing

“…The proposed strategy relies on the threshold as a guide to truncate the bit-widths of the operators at various stages of the HCD pipeline with only marginal loss of accuracy. …”
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Guided Co-segmentation network for fast video object segmentation by Liu, Weide, Lin, Guosheng, Zhang, Tianyi, Liu, Zichuan

“…In our work, we focus on online semi-supervised video object segmentation. We propose a GCSeg (Guided Co-Segmentation) Network which is mainly composed of a Reference Module and a Co-segmentation Module, to simultaneously incorporate the short-term, middle-term, and long-term temporal inter-frame relationships. …”
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Entropy guided attention network for weakly-supervised action localization by Cheng, Yi, Sun, Ying, Fan, Hehe, Zhuo, Tao, Lim, Joo-Hwee, Kankanhalli, Mohan

“…To address this problem, we propose an Entropy Guided Attention Network (EGA-Net) to treat background frames as out-of-domain samples. …”
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Few-shot forgery detection via guided adversarial interpolation by Qiu, Haonan, Chen, Siyu, Gan, Bei, Wang, Kun, Shi, Huafeng, Shao, Jing, Liu, Ziwei

“…In this work, we address the few-shot forgery detection problem by 1) designing a comprehensive benchmark based on coverage analysis among various forgery approaches, and 2) proposing Guided Adversarial Interpolation (GAI). Our key insight is that there exist transferable distribution characteristics between majority and minority forgery classes1. …”
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Superpixel guided deep-sparse-representation learning for hyperspectral image classification by Fan, Jiayuan, Chen, Tao, Lu, Shijian

“…This paper presents a new technique for hyperspectral image (HSI) classification by using superpixel guided deep-sparse-representation learning. The proposed technique constructs a hierarchical architecture by exploiting the sparse coding to learn the HSI representation. …”
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Multi-lead model-based ECG signal denoising by guided filter by Hao, Huaqing, Liu, Ming, Xiong, Peng, Du, Haiman, Zhang, Hong, Lin, Feng, Hou, Zengguang, Liu, Xiuling

“…For each person, a patient-specific statistical model will be constructed by sparse autoencoder (SAE) which can effectively preserve the detailed signal features. Thus, the guided signal producing by the statistical model can perform well in the guided filter. …”
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Edge detection guide network for semantic segmentation of remote-sensing images by Jin, Jianhui, Zhou, Wujie, Yang, Rongwang, Ye, Lv, Yu, Lu

“…This method aids multimodal information fusion by utilizing spatial information contained in the boundary. An edge detection guide module is included in the feature extraction stage to realize the boundary information through the fusion of details and semantics between high-level and low-level features. …”
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MEGACare: knowledge-guided multi-view hypergraph predictive framework for healthcare by Wu, Jialun, He, Kai, Mao, Rui, Li, Chen, Cambria, Erik

“…To address these limitations, we proposed a knowledge-guided Multi-viEw hyperGrAph predictive framework (MEGACare) for diagnosis prediction and medication recommendation. …”
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GeoConv: geodesic guided convolution for facial action unit recognition by Chen, Yuedong, Song, Guoxian, Shao, Zhiwen, Cai, Jianfei, Cham, Tat-Jen, Zheng, Jianmin

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Hybrid-feature-guided lung nodule type classification on CT images by Yuan, Jingjing, Liu, Xinglong, Hou, Fei, Qin, Hong, Hao, Aimin

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Visual-quality guided global backlight dimming for video display on mobile devices by Yeh, Chia-Hung, Lo, Kyle Shih-Huang, Lin, Weisi

“…This proposes a visual-quality guided global backlight dimming (VQG-GBD) algorithm to reduce the power consumption of liquid-crystal display on mobile devices. …”
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A hybrid cross-entropy guided genetic algorithm for scheduling multi-energy systems by Dan, Mainak, Srinivasan, Seshadhri, Sundaram, Suresh

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Mind the portability : a warriors guide through realistic profiled side-channel analysis by Bhasin, Shivam, Chattopadhyay, Anupam, Heuser, Annelie, Jap, Dirmanto, Picek, Stjepan, Shrivastwa, Ritu Ranjan

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Guide them through : an automatic crowd control framework using multi-objective genetic programming by Hu, Nan, Zhong, Jinghui, Zhou, Joey Tianyi, Zhou, Suiping, Cai, Wentong, Monterola, Christopher

“…In particular, based on the sensed local crowd densities at different segments, our framework is capable of generating control strategies that guide the individuals on when and where to slow down for optimal overall crowd flow in realtime, quantitatively measured by multiple objectives such as shorter travel time and less congestion along the path. …”
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SGDNet : an end-to-end saliency-guided deep neural network for no-reference image quality assessment by Yang, Sheng, Jiang, Qiuping, Lin, Weisi, Wang, Yongtao

“…We propose an end-to-end saliency-guided deep neural network (SGDNet) for no-reference image quality assessment (NR-IQA). …”
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