activity
20232025
most citedV-Express: Conditional Dropout for Progressive Training of Portrait Video Generation

5 citations · 5 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2025

Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering

Zifeng Cheng, Zhonghui Wang, Yuchen Fu +4

Extracting sentence embeddings from large language models (LLMs) is a practical direction, as it requires neither additional data nor fine-tuning. Previous studies usually focus on…

cs.CV2025

Graph Network for Sign Language Tasks

Shiwei Gan, Yafeng Yin, Zhiwei Jiang +3

Recent advances in sign language research have benefited from CNN-based backbones, which are primarily transferred from traditional computer vision tasks (\eg object identification…

cs.SE2025

LocAgent: Graph-Guided LLM Agents for Code Localization

Zhaoling Chen, Xiangru Tang, Gangda Deng +6

Code localization--identifying precisely where in a codebase changes need to be made--is a fundamental yet challenging task in software maintenance. Existing approaches struggle to…

cs.CV20245 cited

V-Express: Conditional Dropout for Progressive Training of Portrait Video Generation

Cong Wang, Kuan Tian, Jun Zhang +7

In the field of portrait video generation, the use of single images to generate portrait videos has become increasingly prevalent. A common approach involves leveraging generative…

cs.CL2023

Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling

Zifeng Cheng, Qingyu Zhou, Zhiwei Jiang +3

Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or s…