activity
20162024
most citedAdaptive Siamese Tracking with a Compact Latent Network

50 citations · 239 across the 41 of their papers we have counts for

collaborators

34 papers

cs.HC2024

BattleAgent: Multi-modal Dynamic Emulation on Historical Battles to Complement Historical Analysis

Shuhang Lin, Wenyue Hua, Lingyao Li +7

This paper presents BattleAgent, an emulation system that combines the Large Vision-Language Model and Multi-agent System. This novel system aims to simulate complex dynamic intera…

cs.CV20241 cited

The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking

Yuying Li, Zeyan Liu, Junyi Zhao +4

Generative AI models can produce high-quality images based on text prompts. The generated images often appear indistinguishable from images generated by conventional optical photog…

cs.CV20234 cited

GPT-4V(ision) as A Social Media Analysis Engine

Hanjia Lyu, Jinfa Huang, Daoan Zhang +6

Recent research has offered insights into the extraordinary capabilities of Large Multimodal Models (LMMs) in various general vision and language tasks. There is growing interest i…

cs.CV20231 cited

OpenLEAF: Open-Domain Interleaved Image-Text Generation and Evaluation

Jie An, Zhengyuan Yang, Linjie Li +5

This work investigates a challenging task named open-domain interleaved image-text generation, which generates interleaved texts and images following an input query. We propose a n…

cs.LG2023

Deceptive Fairness Attacks on Graphs via Meta Learning

Jian Kang, Yinglong Xia, Ross Maciejewski +2

We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively? W…

cs.CL20232 cited

Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets

Jinsheng Pan, Zichen Wang, Weihong Qi +2

Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters. While prior…