19 citations · 94 across the 23 of their papers we have counts for
23 papers
Examining the Effect of Pre-training on Time Series Classification
Jiashu Pu, Shiwei Zhao, Ling Cheng +4
Although the pre-training followed by fine-tuning paradigm is used extensively in many fields, there is still some controversy surrounding the impact of pre-training on the fine-tu…
Sudowoodo: a Chinese Lyric Imitation System with Source Lyrics
Yongzhu Chang, Rongsheng Zhang, Lin Jiang +3
Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control su…
I-WAS: a Data Augmentation Method with GPT-2 for Simile Detection
Yongzhu Chang, Rongsheng Zhang, Jiashu Pu
Simile detection is a valuable task for many natural language processing (NLP)-based applications, particularly in the field of literature. However, existing research on simile det…
DiffColor: Toward High Fidelity Text-Guided Image Colorization with Diffusion Models
Jianxin Lin, Peng Xiao, Yijun Wang +2
Recent data-driven image colorization methods have enabled automatic or reference-based colorization, while still suffering from unsatisfactory and inaccurate object-level color co…
Beyond First Impressions: Integrating Joint Multi-modal Cues for Comprehensive 3D Representation
Haowei Wang, Jiji Tang, Jiayi Ji +8
In recent years, 3D understanding has turned to 2D vision-language pre-trained models to overcome data scarcity challenges. However, existing methods simply transfer 2D alignment s…
Neighborhood-based Hard Negative Mining for Sequential Recommendation
Lu Fan, Jiashu Pu, Rongsheng Zhang +1
Negative sampling plays a crucial role in training successful sequential recommendation models. Instead of merely employing random negative sample selection, numerous strategies ha…
Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting
Shubin Huang, Qiong Wu, Yiyi Zhou +4
Pre-trained language models (PLMs) have played an increasing role in multimedia research. In terms of vision-language (VL) tasks, they often serve as a language encoder and still r…
PromptNER: Prompt Locating and Typing for Named Entity Recognition
Yongliang Shen, Zeqi Tan, Shuhui Wu +5
Prompt learning is a new paradigm for utilizing pre-trained language models and has achieved great success in many tasks. To adopt prompt learning in the NER task, two kinds of met…
Structure-CLIP: Towards Scene Graph Knowledge to Enhance Multi-modal Structured Representations
Yufeng Huang, Jiji Tang, Zhuo Chen +8
Large-scale vision-language pre-training has achieved significant performance in multi-modal understanding and generation tasks. However, existing methods often perform poorly on i…
Tailoring Language Generation Models under Total Variation Distance
Haozhe Ji, Pei Ke, Zhipeng Hu +2
The standard paradigm of neural language generation adopts maximum likelihood estimation (MLE) as the optimizing method. From a distributional view, MLE in fact minimizes the Kullb…
Facial Action Unit Detection and Intensity Estimation from Self-supervised Representation
Bowen Ma, Rudong An, Wei Zhang +6
As a fine-grained and local expression behavior measurement, facial action unit (FAU) analysis (e.g., detection and intensity estimation) has been documented for its time-consuming…
Generating Coherent Narratives by Learning Dynamic and Discrete Entity States with a Contrastive Framework
Jian Guan, Zhenyu Yang, Rongsheng Zhang +2
Despite advances in generating fluent texts, existing pretraining models tend to attach incoherent event sequences to involved entities when generating narratives such as stories a…
Probing Simile Knowledge from Pre-trained Language Models
Weijie Chen, Yongzhu Chang, Rongsheng Zhang +6
Simile interpretation (SI) and simile generation (SG) are challenging tasks for NLP because models require adequate world knowledge to produce predictions. Previous works have empl…
LaMemo: Language Modeling with Look-Ahead Memory
Haozhe Ji, Rongsheng Zhang, Zhenyu Yang +2
Although Transformers with fully connected self-attentions are powerful to model long-term dependencies, they are struggling to scale to long texts with thousands of words in langu…
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks
Ziyang Luo, Yadong Xi, Jing Ma +4
Since 2017, the Transformer-based models play critical roles in various downstream Natural Language Processing tasks. However, a common limitation of the attention mechanism utiliz…
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning
Ziyang Luo, Zhipeng Hu, Yadong Xi +2
Image Captioning is a traditional vision-and-language task that aims to generate the language description of an image. Recent studies focus on scaling up the model size and the num…
Youling: an AI-Assisted Lyrics Creation System
Rongsheng Zhang, Xiaoxi Mao, Le Li +6
Recently, a variety of neural models have been proposed for lyrics generation. However, most previous work completes the generation process in a single pass with little human inter…
A Frustratingly Simple Approach for End-to-End Image Captioning
Ziyang Luo, Yadong Xi, Rongsheng Zhang +1
Image Captioning is a fundamental task to join vision and language, concerning about cross-modal understanding and text generation. Recent years witness the emerging attention on i…
Unsupervised Domain Adaptation with Adapter
Rongsheng Zhang, Yinhe Zheng, Xiaoxi Mao +1
Unsupervised domain adaptation (UDA) with pre-trained language models (PrLM) has achieved promising results since these pre-trained models embed generic knowledge learned from vari…
Stylized Dialogue Response Generation Using Stylized Unpaired Texts
Yinhe Zheng, Zikai Chen, Rongsheng Zhang +3
Generating stylized responses is essential to build intelligent and engaging dialogue systems. However, this task is far from well-explored due to the difficulties of rendering a p…
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data
Rongsheng Zhang, Yinhe Zheng, Jianzhi Shao +3
Recent advances in open-domain dialogue systems rely on the success of neural models that are trained on large-scale data. However, collecting large-scale dialogue data is usually…
A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data
Yinhe Zheng, Rongsheng Zhang, Xiaoxi Mao +1
Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both…
Stochastic Online Control for Energy-Harvesting Wireless Networks with Battery Imperfections
Xin Wang, Tianhui Ma, Rongsheng Zhang +1
In energy harvesting (EH) network, the energy storage devices (i.e., batteries) are usually not perfect. In this paper, we consider a practical battery model with finite battery ca…