Unverified50% confidenceConceptExact time
The knowledge framework of vision foundation models can be divided into two major components: pre-training and downstream task adaptation.
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9/25/2026
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UnverifiedThe pretraining and fine-tuning paradigm originated from NLP and computer vision, with its core being large-scale data pretraining to learn general low-level features, and downstream tasks adapted using a small amount of labeled data.75% similarUnverifiedWhen fine-tuning a visual large model for downstream tasks, one can choose to fine-tune only the backend LLM, only the frontend Encoder, both parts, or specify particular layers and modules to train.74% similarUnverified大模型的工程落地分为训练和推理两个核心环节74% similarVerifiedSelf-supervised learning is the core paradigm of the pre-training phase, where the model constructs training signals from the data itself through a next-token prediction task.71% similarUnverifiedMethods such as freezing LLM backbone parameters during the visual alignment training phase, staged training, data ratio replay, and LoRA can mitigate catastrophic forgetting in multimodal training.70% similar
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