activity
20232025
most citedSelf-supervised Representation Learning From Random Data Projectors

3 citations · 4 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CV2025

IntentVCNet: Bridging Spatio-Temporal Gaps for Intention-Oriented Controllable Video Captioning

Tianheng Qiu, Jingchun Gao, Jingyu Li +6

Intent-oriented controlled video captioning aims to generate targeted descriptions for specific targets in a video based on customized user intent. Current Large Visual Language Mo…

cs.LG2025

SCFCRC: Simultaneously Counteract Feature Camouflage and Relation Camouflage for Fraud Detection

Xiaocheng Zhang, Zhuangzhuang Ye, GuoPing Zhao +2

In fraud detection, fraudsters often interact with many benign users, camouflaging their features or relations to hide themselves. Most existing work concentrates solely on either…

cs.CL2024

TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking

Xiaocheng Zhang, Xi Wang, Yifei Lu +5

With the surge of online misinformation, Large Language Models (LLMs) and Reasoning Large Language Models (RLMs) serving as Automatic Fact-Checking (AFC) systems have emerged as a…

cs.CL2024

Self-Evolutionary Large Language Models through Uncertainty-Enhanced Preference Optimization

Jianing Wang, Yang Zhou, Xiaocheng Zhang +2

Iterative preference optimization has recently become one of the de-facto training paradigms for large language models (LLMs), but the performance is still underwhelming due to too…

cs.CL2023★ 1 cited

EHR Interaction Between Patients and AI: NoteAid EHR Interaction

Xiaocheng Zhang, Zonghai Yao, Hong Yu

With the rapid advancement of Large Language Models (LLMs) and their outstanding performance in semantic and contextual comprehension, the potential of LLMs in specialized domains…

cs.LG2023★ 3 cited

Self-supervised Representation Learning From Random Data Projectors

Yi Sui, Tongzi Wu, Jesse C. Cresswell +5

Self-supervised representation learning~(SSRL) has advanced considerably by exploiting the transformation invariance assumption under artificially designed data augmentations. Whil…