Jun 2, 2023 · We propose a novel regularization technique, ie, Calibrating Multimodal Learning (CML) regularization, to calibrate the predictive confidence of previous ...
We propose a regularization strategy to calibrate the confidence of various multimodal learning methods, and then conduct extensive experiments to show the.
Feb 13, 2023 · This paper identifies a pathology in multimodal classifiers -- that they can become more rather than less confident when modalities are removed.
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Jun 2, 2023 · Calibrating Multimodal Learning ... To address this issue, we propose a novel regular- ization technique called Calibrating Multimodal Learning.
Jul 23, 2023 · Accordingly, we propose a novel regularization technique, i.e., Calibrating Multimodal Learning (CML) regularization, to calibrate the ...
Multimodal machine learning has achieved remarkable progress in a wide range of scenarios. However, the reliability of multimodal learning remains largely ...
This repository contains the code of Calibrating Multimodal Learning (CML). Here we provide a demo and detailed instructions for constructing CML on several ...
Specifically, to ensure learning models do not depend on spurious correlations, which can lead to high error rates in certain data groups, we align with ...
As a piloting study, this work focuses on exploring mitigating the reliance on spurious features for CLIP without using any group annotation. To this end, we ...
Jul 10, 2024 · MMEvalPro introduces a new approach to multimodal evaluation by augmenting existing benchmark questions with two additional anchor questions.