20 citations · 58 across the 9 of their papers we have counts for
9 papers
Label Semantic Expansion via Label Guided Neural Topic Modeling
Haojia Zheng, Yuyin Lu, Juntian Huang +4
Topic models are widely used for content analysis, where users often analyze corpora around predefined labels rather than unordered latent topics. Existing label-aware topic models…
Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Sen Deng, Yidan Feng, Mingqiang Wei +5
We present a novel direction-aware feature-level frequency decomposition network for single image deraining. Compared with existing solutions, the proposed network has three compel…
Sketch-based Normal Map Generation with Geometric Sampling
Yi He, Haoran Xie, Chao Zhang +2
Normal map is an important and efficient way to represent complex 3D models. A designer may benefit from the auto-generation of high quality and accurate normal maps from freehand…
Context Reinforced Neural Topic Modeling over Short Texts
Jiachun Feng, Zusheng Zhang, Cheng Ding +2
As one of the prevalent topic mining tools, neural topic modeling has attracted a lot of interests for the advantages of high efficiency in training and strong generalisation abili…
Handling Collocations in Hierarchical Latent Tree Analysis for Topic Modeling
Leonard K. M. Poon, Nevin L. Zhang, Haoran Xie +1
Topic modeling has been one of the most active research areas in machine learning in recent years. Hierarchical latent tree analysis (HLTA) has been recently proposed for hierarchi…
MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images
Yiyang Shen, Yidan Feng, Sen Deng +4
Rain severely hampers the visibility of scene objects when images are captured through glass in heavily rainy days. We observe three intriguing phenomenons that, 1) rain is a mixtu…