Vision and Language

SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data
SELMA improves T2I models by fine-tuning on automatically generated multi-skill image-text datasets, with skill-specific LoRA expert learning & merging. - NeurIPS 2024
Jialu Li, Jaemin Cho, Yi-Lin Sung, Jaehong Yoon, Mohit Bansal
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SELMA: Learning and Merging Skill-Specific Text-to-Image Experts with Auto-Generated Data
DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning
Using LLM (GPT-4) to generate a ‘diagram plan’ for fine-grained layouts (object/text labels/arrows, etc.) and render in either raster images (via diffusion) and vector graphics (via PowerPoint / Inkscape or any tools). - COLM 2024
Abhay Zala, Han Lin, Jaemin Cho, Mohit Bansal
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DiagrammerGPT: Generating Open-Domain, Open-Platform Diagrams via LLM Planning
VideoDirectorGPT: Consistent Multi-Scene Video Generation via LLM-Guided Planning
Using LLM (GPT-4) to generate a ‘video plan’ for consistent multi-scene video generation - COLM 2024
Han Lin, Abhay Zala, Jaemin Cho, Mohit Bansal
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VideoDirectorGPT: Consistent Multi-Scene Video Generation via LLM-Guided Planning
DOCCI: Descriptions of Connected and Contrasting Images
High-quality, long, human-annotated descriptions of 15K images - ECCV 2024
Yasumasa Onoe, Sunayana Rane, Zachary Berger, Yonatan Bitton, Jaemin Cho, Roopal Garg, Alexander Ku, Zarana Parekh, Jordi Pont-Tuset, Garrett Tanzer, Su Wang, Jason Baldridge
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DOCCI: Descriptions of Connected and Contrasting Images
Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
a New Diagnostic Benchmark (LayoutBench) and a new Baseline model (IterInpaint) for Layout-Guided Image Generation - CVPR Workshop 2024 (Oral)
Jaemin Cho, Linjie Li, Zhengyuan Yang, Zhe Gan, Lijuan Wang, Mohit Bansal
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Diagnostic Benchmark and Iterative Inpainting for Layout-Guided Image Generation
Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training
CRG is a training-free method that guides VLMs to help understand the visual prompts, by contrasting the outputs with & without visual prompts. - ECCV 2024
David Wan, Jaemin Cho, Elias Stengel-Eskin, Mohit Bansal
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Contrastive Region Guidance: Improving Grounding in Vision-Language Models without Training
Davidsonian Scene Graph: Improving Reliability in Fine-Grained Evaluation for Text-to-Image Generation
Reliable QG/A framework for T2I Evaluation based on Davidsonian Semantics - ICLR 2024
Jaemin Cho, Yushi Hu, Roopal Garg, Peter Anderson, Ranjay Krishna, Jason Baldridge, Mohit Bansal, Jordi Pont-Tuset, Su Wang
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Davidsonian Scene Graph: Improving Reliability in Fine-Grained Evaluation for Text-to-Image Generation
Self-Chained Image-Language Model for Video Localization and Question Answering
To handle video QA, we self-chain BLIP-2 for 2-stage inference (localize+QA) & refining localization via QA feedback - NeurIPS 2023
Shoubin Yu, Jaemin Cho, Prateek Yadav, Mohit Bansal
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Self-Chained Image-Language Model for Video Localization and Question Answering
Paxion: Patching Action Knowledge in Video-Language Foundation Models
Analyzing and patching action knowledge in video-language models - NeurIPS 2023 (Spotlight)
Zhenhailong Wang, Ansel Blume, Sha Li, Genglin Liu, Jaemin Cho, Zineng Tang, Mohit Bansal, Heng Ji
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Paxion: Patching Action Knowledge in Video-Language Foundation Models
TVLT: Textless Vision-Language Transformer
Vision-and-Language modeling without text, by using a transformer which takes only raw visual and audio inputs - NeurIPS 2022 (Oral)
Zineng Tang, Jaemin Cho, Yixin Nie, Mohit Bansal
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TVLT: Textless Vision-Language Transformer