155 citations · 244 across the 13 of their papers we have counts for
13 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…
Label Supervised LLaMA Finetuning
Zongxi Li, Xianming Li, Yuzhang Liu +5
The recent success of Large Language Models (LLMs) has gained significant attention in both academia and industry. Substantial efforts have been made to enhance the zero- and few-s…
Recurrent Attention Networks for Long-text Modeling
Xianming Li, Zongxi Li, Xiaotian Luo +5
Self-attention-based models have achieved remarkable progress in short-text mining. However, the quadratic computational complexities restrict their application in long text proces…
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…
Incorporating Effective Global Information via Adaptive Gate Attention for Text Classification
Xianming Li, Zongxi Li, Yingbin Zhao +2
The dominant text classification studies focus on training classifiers using textual instances only or introducing external knowledge (e.g., hand-craft features and domain expert k…
DRD-Net: Detail-recovery Image Deraining via Context Aggregation Networks
Sen Deng, Mingqiang Wei, Jun Wang +3
Image deraining is a fundamental, yet not well-solved problem in computer vision and graphics. The traditional image deraining approaches commonly behave ineffectively in medium an…
On the Effectiveness of Least Squares Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie +3
Unsupervised learning with generative adversarial networks (GANs) has proven to be hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cr…
AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie
Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-do…
Least Squares Generative Adversarial Networks
Xudong Mao, Qing Li, Haoran Xie +3
Unsupervised learning with generative adversarial networks (GANs) has proven hugely successful. Regular GANs hypothesize the discriminator as a classifier with the sigmoid cross en…