How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary Investigation

arXiv:2312.07424v3 Announce Type: replace
Abstract: In machine learning, generalization against distribution shifts -- where deployment conditions diverge from the training scenarios -- is crucial, particularly in fields like climate modeling, biomedicine, and autonomous driving. The emergence of foundation models, distinguished by their extensive pretraining and task versatility, has led to an increased interest in their adaptability to distribution shifts. GPT-4V(ision) acts as the most advanced publicly accessible multimodal foundation model, with extensive applications across various domains, including anomaly detection, video …

adapt arxiv cs.ai cs.cv cs.lg distribution gpt gpt-4v investigation type