The landscape of publicly available vision foundation models (VFMs) such as CLIP and
SAM is expanding rapidly. VFMs are endowed with distinct capabilities stemming from their …

Model merging is an efficient empowerment technique in the machine learning community
that does not require the collection of raw training data and does not require expensive …

The rapid development of AI systems has been greatly influenced by the emergence of
foundation models. A common approach for targeted problems involves fine-tuning these …

Foundation models have demonstrated remarkable performance across various tasks,
primarily due to their abilities to comprehend instructions and access extensive, high-quality …

Large diffusion-based Text-to-Image (T2I) models have shown impressive generative
powers for text-to-image generation and spatially conditioned image generation. We can …

The rapid expansion of the open-source language model landscape presents an opportunity
to merge the competencies of these model checkpoints by combining their parameters …

Deep model fusion/merging is an emerging technique that merges the parameters or
predictions of multiple deep learning models into a single one. It combines the abilities of …

Multi-task learning (MTL) aims to empower a model to tackle multiple tasks simultaneously.
A recent development known as task arithmetic has revealed that several models, each fine …

Recent model merging methods demonstrate that the parameters of fully-finetuned models
specializing in distinct tasks can be combined into one model capable of solving all tasks …

Model merging, which combines multiple models into a single model, has gained increasing
popularity in recent years. By efficiently integrating the capabilities of various models without …