LLM

VideoRepair: Improving Text-to-Video Generation via Misalignment Evaluation and Localized Refinement
A new automatic refinement framework for T2V generation based on fine-grained text-video misalignment evaluation and localized refinement
Daeun Lee, Jaehong Yoon, Jaemin Cho, Mohit Bansal
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VideoRepair: Improving Text-to-Video Generation via Misalignment Evaluation and Localized Refinement
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
DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
A new testbed of teacher environments for data generation agents for diverse tasks.
Zaid Khan, Elias Stengel-Eskin, Jaemin Cho, Mohit Bansal
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DataEnvGym: Data Generation Agents in Teacher Environments with Student Feedback
EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents
EnvGen is a novel framework that uses LLMs to adaptively create training environments to help smaller embodied RL agents learn useful skills that they are weak at. - COLM 2024
Abhay Zala, Jaemin Cho, Han Lin, Jaehong Yoon, Mohit Bansal
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EnvGen: Generating and Adapting Environments via LLMs for Training Embodied Agents