RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned
  Vision-Language Models
Fine-tuned vision-language models (VLMs) often capture spurious correlations
between image features and textual attributes, resulting in degraded zero-shot
performance at test time. Existing approaches for addressing spurious
correlations (i) primarily operate at the global image-level rather than
intervening directly on fine-grained image features and (ii) are predominantly
designed for unimodal settings. In this work, we present RaVL, which takes a
fine-grained perspective on VLM robustness by discovering and mitigating
spurious correlations using local image features rather than operating at the
global image level. Given a fine-tuned VLM, RaVL first discovers spurious
correlations by leveraging a region-level clustering approach to identify
precise image features contributing to zero-shot classification errors. Then,
RaVL mitigates the identified spurious correlation with a novel region-aware
loss function that enables the VLM to focus on relevant regions and ignore
spurious relationships during fine-tuning. We evaluate RaVL on 654 VLMs with
various model architectures, data domains, and learned spurious correlations.
Our results show that RaVL accurately discovers (191% improvement over the
closest baseline) and mitigates (8.2% improvement on worst-group image
classification accuracy) spurious correlations. Qualitative evaluations on
general-domain and medical-domain VLMs confirm our findings.
673234ee6c51a40ff8739742
Maya Varma
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2024-11-15T09:27:59.630Z
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Maya Varma
user
mvarma
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Jean-Benoit Delbrouck
claimed_verified
2024-11-16T19:47:03.827Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/62716952bcef985363db8485/zJPPo5xlwZRJdEuwYsYKp.jpeg
JB D.
user
IAMJB
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Zhihong Chen
673234ee6c51a40ff8739745
Akshay Chaudhari
extracted_pending
2024-11-11T16:46:41.296Z
6236533b76c8a780323af640
/avatars/18078ad26aa44312a4927160216e5943.svg
Akshay Chaudhari
user
akshaysc
673234ee6c51a40ff8739746
Curtis Langlotz
extracted_pending
2024-11-11T16:46:41.296Z
62867edd1d99648808888d7b
/avatars/22d764782c5169634deec6f43ece7500.svg
Curt Langlotz
user
cplanglotz
RaVL: Discovering and Mitigating Spurious Correlations in Fine-Tuned
  Vision-Language Models
Fine-tuned vision-language models (VLMs) often capture spurious correlations
between image features and textual attributes, resulting in degraded zero-shot
performance at test time. Existing approaches for addressing spurious
correlations (i) primarily operate at the global image-level rather than
intervening directly on fine-grained image features and (ii) are predominantly
designed for unimodal settings. In this work, we present RaVL, which takes a
fine-grained perspective on VLM robustness by discovering and mitigating
spurious correlations using local image features rather than operating at the
global image level. Given a fine-tuned VLM, RaVL first discovers spurious
correlations by leveraging a region-level clustering approach to identify
precise image features contributing to zero-shot classification errors. Then,
RaVL mitigates the identified spurious correlation with a novel region-aware
loss function that enables the VLM to focus on relevant regions and ignore
spurious relationships during fine-tuning. We evaluate RaVL on 654 VLMs with
various model architectures, data domains, and learned spurious correlations.
Our results show that RaVL accurately discovers (191% improvement over the
closest baseline) and mitigates (8.2% improvement on worst-group image
classification accuracy) spurious correlations. Qualitative evaluations on
general-domain and medical-domain VLMs confirm our findings.
RaVL improves VLM robustness by identifying and mitigating spurious correlations at the local image feature level during fine-tuning.
vision-language models VLMs spurious correlations region-level clustering region-aware loss function zero-shot classification fine-tuning