56 citations · 176 across the 18 of their papers we have counts for
18 papers
Air-Decoding: Attribute Distribution Reconstruction for Decoding-Time Controllable Text Generation
Tianqi Zhong, Quan Wang, Jingxuan Han +2
Controllable text generation (CTG) aims to generate text with desired attributes, and decoding-time-based methods have shown promising performance on this task. However, in this pa…
DreamIdentity: Improved Editability for Efficient Face-identity Preserved Image Generation
Zhuowei Chen, Shancheng Fang, Wei Liu +4
While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centric images, an intractable problem is how to preserve the face identity for con…
Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation
Mengqi Huang, Zhendong Mao, Quan Wang +1
Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image gener…
Towards Accurate Image Coding: Improved Autoregressive Image Generation with Dynamic Vector Quantization
Mengqi Huang, Zhendong Mao, Zhuowei Chen +1
Existing vector quantization (VQ) based autoregressive models follow a two-stage generation paradigm that first learns a codebook to encode images as discrete codes, and then compl…
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
Benfeng Xu, An Yang, Junyang Lin +4
The answering quality of an aligned large language model (LLM) can be drastically improved if treated with proper crafting of prompts. In this paper, we propose ExpertPrompting to…
Inductive Relation Prediction from Relational Paths and Context with Hierarchical Transformers
Jiaang Li, Quan Wang, Zhendong Mao
Relation prediction on knowledge graphs (KGs) is a key research topic. Dominant embedding-based methods mainly focus on the transductive setting and lack the inductive ability to g…
NN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference
Benfeng Xu, Quan Wang, Zhendong Mao +3
In-Context Learning (ICL), which formulates target tasks as prompt completion conditioned on in-context demonstrations, has become the prevailing utilization of LLMs. In this paper…
ABINet++: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Spotting
Shancheng Fang, Zhendong Mao, Hongtao Xie +3
Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for ch…
Intra-class Adaptive Augmentation with Neighbor Correction for Deep Metric Learning
Zheren Fu, Zhendong Mao, Bo Hu +2
Deep metric learning aims to learn an embedding space, where semantically similar samples are close together and dissimilar ones are repelled against. To explore more hard and info…
UniRel: Unified Representation and Interaction for Joint Relational Triple Extraction
Wei Tang, Benfeng Xu, Yuyue Zhao +4
Relational triple extraction is challenging for its difficulty in capturing rich correlations between entities and relations. Existing works suffer from 1) heterogeneous representa…
Improving Chinese Spelling Check by Character Pronunciation Prediction: The Effects of Adaptivity and Granularity
Jiahao Li, Quan Wang, Zhendong Mao +3
Chinese spelling check (CSC) is a fundamental NLP task that detects and corrects spelling errors in Chinese texts. As most of these spelling errors are caused by phonetic similarit…
DSE-GAN: Dynamic Semantic Evolution Generative Adversarial Network for Text-to-Image Generation
Mengqi Huang, Zhendong Mao, Penghui Wang +2
Text-to-image generation aims at generating realistic images which are semantically consistent with the given text. Previous works mainly adopt the multi-stage architecture by stac…
Read Like Humans: Autonomous, Bidirectional and Iterative Language Modeling for Scene Text Recognition
Shancheng Fang, Hongtao Xie, Yuxin Wang +2
Linguistic knowledge is of great benefit to scene text recognition. However, how to effectively model linguistic rules in end-to-end deep networks remains a research challenge. In…
Entity Structure Within and Throughout: Modeling Mention Dependencies for Document-Level Relation Extraction
Benfeng Xu, Quan Wang, Yajuan Lyu +2
Entities, as the essential elements in relation extraction tasks, exhibit certain structure. In this work, we formulate such structure as distinctive dependencies between mention p…
Image Captioning with Context-Aware Auxiliary Guidance
Zeliang Song, Xiaofei Zhou, Zhendong Mao +1
Image captioning is a challenging computer vision task, which aims to generate a natural language description of an image. Most recent researches follow the encoder-decoder framewo…
Overcoming Language Priors with Self-supervised Learning for Visual Question Answering
Xi Zhu, Zhendong Mao, Chunxiao Liu +3
Most Visual Question Answering (VQA) models suffer from the language prior problem, which is caused by inherent data biases. Specifically, VQA models tend to answer questions (e.g.…
Graph Structured Network for Image-Text Matching
Chunxiao Liu, Zhendong Mao, Tianzhu Zhang +3
Image-text matching has received growing interest since it bridges vision and language. The key challenge lies in how to learn correspondence between image and text. Existing works…
Focus Your Attention: A Bidirectional Focal Attention Network for Image-Text Matching
Chunxiao Liu, Zhendong Mao, An-An Liu +3
Learning semantic correspondence between image and text is significant as it bridges the semantic gap between vision and language. The key challenge is to accurately find and corre…