activity
20232025
most citedNN Prompting: Beyond-Context Learning with Calibration-Free Nearest Neighbor Inference

13 citations · 31 across the 12 of their papers we have counts for

collaborators

9 papers

cs.CV202411 cited

Sentiment-oriented Transformer-based Variational Autoencoder Network for Live Video Commenting

Fengyi Fu, Shancheng Fang, Weidong Chen +1

Automatic live video commenting is with increasing attention due to its significance in narration generation, topic explanation, etc. However, the diverse sentiment consideration o…

cs.CV20241 cited

RealCustom: Narrowing Real Text Word for Real-Time Open-Domain Text-to-Image Customization

Mengqi Huang, Zhendong Mao, Mingcong Liu +2

Text-to-image customization, which aims to synthesize text-driven images for the given subjects, has recently revolutionized content creation. Existing works follow the pseudo-word…

cs.CV2024

Gradual Residuals Alignment: A Dual-Stream Framework for GAN Inversion and Image Attribute Editing

Hao Li, Mengqi Huang, Lei Zhang +3

GAN-based image attribute editing firstly leverages GAN Inversion to project real images into the latent space of GAN and then manipulates corresponding latent codes. Recent invers…

cs.CL20242 cited

Benchmarking Large Language Models on Controllable Generation under Diversified Instructions

Yihan Chen, Benfeng Xu, Quan Wang +2

While large language models (LLMs) have exhibited impressive instruction-following capabilities, it is still unclear whether and to what extent they can respond to explicit constra…

cs.CL2023

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…

cs.AI2023

Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation

Jiaang Li, Quan Wang, Yi Liu +2

Representation Learning on Knowledge Graphs (KGs) is essential for downstream tasks. The dominant approach, KG Embedding (KGE), represents entities with independent vectors and fac…