4 citations · 14 across the 32 of their papers we have counts for
11 papers · 1 filter
FlowTool: Controlling Tool Parameter in Image Retouching via Flow Matching
Thanh-Long V. Le, Steven Walton, Seunghyun Yoon +4
Tool-based image editing (image retouching) is commonly formulated with autoregressive multimodal large language models (MLLMs) that sequentially generate reasoning, tool selection…
ViT-AdaLA: Adapting Vision Transformers with Linear Attention
Yifan Li, Seunghyun Yoon, Viet Dac Lai +4
Vision Transformers (ViTs) based vision foundation models (VFMs) have achieved remarkable performance across diverse vision tasks, but suffer from quadratic complexity that limits…
ImageEdit-R1: Boosting Multi-Agent Image Editing via Reinforcement Learning
Yiran Zhao, Yaoqi Ye, Xiang Liu +2
With the rapid advancement of commercial multi-modal models, image editing has garnered significant attention due to its widespread applicability in daily life. Despite impressive…
StreamGaze: Gaze-Guided Temporal Reasoning and Proactive Understanding in Streaming Videos
Daeun Lee, Subhojyoti Mukherjee, Branislav Kveton +6
Streaming video understanding requires models not only to process temporally incoming frames, but also to anticipate user intention for realistic applications such as Augmented Rea…
FIFA: Unified Faithfulness Evaluation Framework for Text-to-Video and Video-to-Text Generation
Liqiang Jing, Viet Lai, Seunghyun Yoon +2
Video Multimodal Large Language Models (VideoMLLMs) have achieved remarkable progress in both Video-to-Text and Text-to-Video tasks. However, they often suffer fro hallucinations,…
Understanding Generative AI Capabilities in Everyday Image Editing Tasks
Mohammad Reza Taesiri, Brandon Collins, Logan Bolton +4
Generative AI (GenAI) holds significant promise for automating everyday image editing tasks, especially following the recent release of GPT-4o on March 25, 2025. However, what subj…
YoChameleon: Personalized Vision and Language Generation
Thao Nguyen, Krishna Kumar Singh, Jing Shi +3
Large Multimodal Models (e.g., GPT-4, Gemini, Chameleon) have evolved into powerful tools with millions of users. However, they remain generic models and lack personalized knowledg…
Toward Robust Hyper-Detailed Image Captioning: A Multiagent Approach and Dual Evaluation Metrics for Factuality and Coverage
Saehyung Lee, Seunghyun Yoon, Trung Bui +2
Multimodal large language models (MLLMs) excel at generating highly detailed captions but often produce hallucinations. Our analysis reveals that existing hallucination detection m…
PEEB: Part-based Image Classifiers with an Explainable and Editable Language Bottleneck
Thang M. Pham, Peijie Chen, Tin Nguyen +3
CLIP-based classifiers rely on the prompt containing a {class name} that is known to the text encoder. Therefore, they perform poorly on new classes or the classes whose names rare…
Scaling Up Video Summarization Pretraining with Large Language Models
Dawit Mureja Argaw, Seunghyun Yoon, Fabian Caba Heilbron +5
Long-form video content constitutes a significant portion of internet traffic, making automated video summarization an essential research problem. However, existing video summariza…
Fine-tuning CLIP Text Encoders with Two-step Paraphrasing
Hyunjae Kim, Seunghyun Yoon, Trung Bui +4
Contrastive language-image pre-training (CLIP) models have demonstrated considerable success across various vision-language tasks, such as text-to-image retrieval, where the model…