How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary
  Investigation
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 understanding, image
generation, and medical diagnosis. However, its robustness against data
distributions remains largely underexplored. Addressing this gap, this study
rigorously evaluates GPT-4V's adaptability and generalization capabilities in
dynamic environments, benchmarking against prominent models like CLIP and
LLaVA. We delve into GPT-4V's zero-shot generalization across 13 diverse
datasets spanning natural, medical, and molecular domains. We further
investigate its adaptability to controlled data perturbations and examine the
efficacy of in-context learning as a tool to enhance its adaptation. Our
findings delineate GPT-4V's capability boundaries in distribution shifts,
shedding light on its strengths and limitations across various scenarios.
Importantly, this investigation contributes to our understanding of how AI
foundation models generalize to distribution shifts, offering pivotal insights
into their adaptability and robustness. Code is publicly available at
https://github.com/jameszhou-gl/gpt-4v-distribution-shift.
657926832f2e4058ae0208cd
Zhongyi Han
claimed_verified
2023-12-13T14:06:07.430Z
65799a1fc9ca8f018ef5fb91
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/X14vTF83NQA_gG4vPrVSh.jpeg
Zhongyi Han
user
zhyhan
657926832f2e4058ae0208ce
Guanglin Zhou
657926832f2e4058ae0208cf
Rundong He
657926832f2e4058ae0208d0
Jindong Wang
admin_assigned
2023-12-13T10:43:49.787Z
6204cc0d522e40b4a18d86e2
/avatars/18daf2de5671e711dc745388dd60569d.svg
Jindong Wang
user
jindongwang
657926832f2e4058ae0208d1
Xing Xie
admin_assigned
2023-12-13T10:44:10.773Z
64785ca82b3730b6b63b75a2
/avatars/b0c4f7fb4d4c556118fa6b52d3615429.svg
Xing Xie
user
xingustc
657926832f2e4058ae0208d2
Tailin Wu
657926832f2e4058ae0208d3
Yilong Yin
admin_assigned
2023-12-13T10:44:48.087Z
640e7a27474aa6f89558d43f
/avatars/be1166944eb2bd252b5fb8de3d56ec74.svg
Yilong Yin
user
starmana66
657926832f2e4058ae0208d4
Salman Khan
657926832f2e4058ae0208d5
Lina Yao
admin_assigned
2023-12-13T10:45:07.625Z
6503be4feda77aafd4b96f92
/avatars/4115a866351738e64250bbfd99ad2761.svg
Lina Yao
user
LinaYao
657926832f2e4058ae0208d6
Tongliang Liu
657926832f2e4058ae0208d7
Kun Zhang
How Well Does GPT-4V(ision) Adapt to Distribution Shifts? A Preliminary
  Investigation
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 understanding, image
generation, and medical diagnosis. However, its robustness against data
distributions remains largely underexplored. Addressing this gap, this study
rigorously evaluates GPT-4V's adaptability and generalization capabilities in
dynamic environments, benchmarking against prominent models like CLIP and
LLaVA. We delve into GPT-4V's zero-shot generalization across 13 diverse
datasets spanning natural, medical, and molecular domains. We further
investigate its adaptability to controlled data perturbations and examine the
efficacy of in-context learning as a tool to enhance its adaptation. Our
findings delineate GPT-4V's capability boundaries in distribution shifts,
shedding light on its strengths and limitations across various scenarios.
Importantly, this investigation contributes to our understanding of how AI
foundation models generalize to distribution shifts, offering pivotal insights
into their adaptability and robustness. Code is publicly available at
https://github.com/jameszhou-gl/gpt-4v-distribution-shift.
The study evaluates GPT-4V's robustness and adaptability to distribution shifts across various domains and perturbations, comparing it to other models and exploring in-context learning enhancements.
GPT-4V multimodal foundation model zero-shot generalization in-context learning distribution shifts CLIP LLaVA