The content discusses a method called "ViSFT" (Vision Supervised Fine-Tuning) that aims to improve the generalization and representation capabilities of vision foundation models through fine-grained supervised fine-tuning.
The key highlights are:
The authors draw inspiration from the natural language processing (NLP) domain, where supervised fine-tuning (SFT) techniques like instruction tuning have been successful in enhancing the performance of large language models.
ViSFT is a two-stage process. In the first stage, the authors train task-specific heads (e.g., object detection, segmentation, captioning) independently while keeping the vision transformer backbone frozen. In the second stage, they introduce LoRA (Low-Rank Adaptation) parameters to the vision transformer backbone and fine-tune the model on the joint tasks.
The authors evaluate the performance of the fine-tuned vision models on various out-of-domain benchmarks, including optical character recognition, grounded object identification, image classification, image-text retrieval, and visual question answering. The results demonstrate significant improvements across these tasks.
Ablation studies are conducted to analyze the impact of different design choices, such as LoRA rank, training data size, and task selection. The authors find that the two-stage training strategy is crucial for effectively transferring fine-grained knowledge to the vision transformer backbone.
Visualization of the attention distribution of the [CLS] token in the vision transformer further supports the claim that ViSFT helps the model capture more fine-grained information from image patches.
Overall, the content showcases the potential of fine-grained supervised fine-tuning in enhancing the generalization capabilities of vision foundation models, providing a simple yet effective approach to improve their performance.
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by Xiaohu Jiang... kl. arxiv.org 04-12-2024
https://arxiv.org/pdf/2401.10222.pdfDybere Forespørgsler