2 citations · 3 across the 16 of their papers we have counts for
9 papers · 1 filter
NICE FACT: Diagnosing and Calibrating VLMs in Quantitative Reasoning for Kinematic Physics
Jian Lan, Zhicheng Liu, Xinpeng Wang +5
The ability to derive precise spatial and physical insights is a cornerstone of vision-language models (VLMs), yet their poor performances in related spatial intelligence tasks suc…
AUVIC: Adversarial Unlearning of Visual Concepts for Multi-modal Large Language Models
Haokun Chen, Jianing Li, Yao Zhang +4
Multimodal Large Language Models (MLLMs) achieve impressive performance once optimized on massive datasets. Such datasets often contain sensitive or copyrighted content, raising si…
DreamPoster: A Unified Framework for Image-Conditioned Generative Poster Design
Xiwei Hu, Haokun Chen, Zhongqi Qi +4
We present DreamPoster, a Text-to-Image generation framework that intelligently synthesizes high-quality posters from user-provided images and text prompts while maintaining conten…
EmoAgent: A Multi-Agent Framework for Diverse Affective Image Manipulation
Qi Mao, Haobo Hu, Yujie He +3
Affective Image Manipulation (AIM) aims to alter visual elements within an image to evoke specific emotional responses from viewers. However, existing AIM approaches rely on rigid…
LLaVA Steering: Visual Instruction Tuning with 500x Fewer Parameters through Modality Linear Representation-Steering
Jinhe Bi, Yujun Wang, Haokun Chen +4
Multimodal Large Language Models (MLLMs) have significantly advanced visual tasks by integrating visual representations into large language models (LLMs). The textual modality, inh…
Towards Artwork Explanation in Large-scale Vision Language Models
Kazuki Hayashi, Yusuke Sakai, Hidetaka Kamigaito +2
Large-scale Vision-Language Models (LVLMs) output text from images and instructions, demonstrating capabilities in text generation and comprehension. However, it has not been clari…