190 citations · 432 across the 45 of their papers we have counts for
9 papers · 1 filter
M3DocRAG: Multi-modal Retrieval is What You Need for Multi-page Multi-document Understanding
Jaemin Cho, Debanjan Mahata, Ozan Irsoy +2
Document visual question answering (DocVQA) pipelines that answer questions from documents have broad applications. Existing methods focus on handling single-page documents with mu…
Self-Correcting Text-to-Video Generation with Misalignment Detection and Localized Refinement
Daeun Lee, Jaehong Yoon, Jaemin Cho +1
Recent text-to-video (T2V) diffusion models have made remarkable progress in generating high-quality videos. However, they often struggle to align with complex text prompts, partic…
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
Zaid Khan, Elias Stengel-Eskin, Jaemin Cho +1
The process of creating training data to teach models is currently driven by humans, who manually analyze model weaknesses and plan how to create data that improves a student model…
Ctrl-Adapter: An Efficient and Versatile Framework for Adapting Diverse Controls to Any Diffusion Model
Han Lin, Jaemin Cho, Abhay Zala +1
ControlNets are widely used for adding spatial control to text-to-image diffusion models with different conditions, such as depth maps, scribbles/sketches, and human poses. However…
DOCCI: Descriptions of Connected and Contrasting Images
Yasumasa Onoe, Sunayana Rane, Zachary Berger +9
Vision-language datasets are vital for both text-to-image (T2I) and image-to-text (I2T) research. However, current datasets lack descriptions with fine-grained detail that would al…
Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts
Qin Liu, Jaemin Cho, Mohit Bansal +1
The goal of interactive image segmentation is to delineate specific regions within an image via visual or language prompts. Low-latency and high-quality interactive segmentation wi…
SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data
Jialu Li, Jaemin Cho, Yi-Lin Sung +2
Recent text-to-image (T2I) generation models have demonstrated impressive capabilities in creating images from text descriptions. However, these T2I generation models often fall sh…
Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training
David Wan, Jaemin Cho, Elias Stengel-Eskin +1
Highlighting particularly relevant regions of an image can improve the performance of vision-language models (VLMs) on various vision-language (VL) tasks by guiding the model to at…
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents
Abhay Zala, Jaemin Cho, Han Lin +2
Recent SOTA approaches for embodied learning via interaction directly employ large language models (LLMs) as agents to determine the next steps in an environment. Due to their worl…