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Training-Free Mitigation of Language Reasoning Degradation After Multimodal Instruction Tuning
Neale Ratzlaff, Man Luo, Xin Su +2
Multimodal models typically combine a powerful large language model (LLM) with a vision encoder and are then trained on multimodal data via instruction tuning. While this process a…
FiVL: A Framework for Improved Vision-Language Alignment through the Lens of Training, Evaluation and Explainability
Estelle Aflalo, Gabriela Ben Melech Stan, Tiep Le +5
Large Vision Language Models (LVLMs) have achieved significant progress in integrating visual and textual inputs for multimodal reasoning. However, a recurring challenge is ensurin…
Refining Text-to-Image Generation: Towards Accurate Training-Free Glyph-Enhanced Image Generation
Sanyam Lakhanpal, Shivang Chopra, Vinija Jain +2
Over the past few years, Text-to-Image (T2I) generation approaches based on diffusion models have gained significant attention. However, vanilla diffusion models often suffer from…