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Found 5 papers, 1 papers with code
Fact :Teaching MLLMs with Faithful, Concise and Transferable Rationales
Their ability to execute intricate compositional reasoning tasks is also constrained, culminating in a stagnation of learning progression for these models.
De-fine: Decomposing and Refining Visual Programs with Auto-Feedback
Visual programming, a modular and generalizable paradigm, integrates different modules and Python operators to solve various vision-language tasks.
Fine-tuning Multimodal LLMs to Follow Zero-shot Demonstrative Instructions
This shortcoming results in MLLMs' underperformance in comprehending demonstrative instructions consisting of multiple, interleaved, and multimodal instructions that demonstrate the required context to complete a task.
Gradient-Regulated Meta-Prompt Learning for Generalizable Vision-Language Models
Prompt tuning, a recently emerging paradigm, enables the powerful vision-language pre-training models to adapt to downstream tasks in a parameter -- and data -- efficient way, by learning the ``soft prompts'' to condition frozen pre-training models.
EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks
Methods: This present study provided a new publicly available Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg).
