ViLU: Learning Vision-Language Uncertainties for Failure Prediction

• URL: http://arxiv.org/abs/2507.07620v3
• Date: Thu, 24 Jul 2025 09:03:07 GMT
• Title: ViLU: Learning Vision-Language Uncertainties for Failure Prediction
• Authors: Marc Lafon, Yannis Karmim, Julio Silva-Rodríguez, Paul Couairon, Clément Rambour, Raphaël Fournier-Sniehotta, Ismail Ben Ayed, Jose Dolz, Nicolas Thome,
• Abstract summary: We introduce ViLU, a new Vision-Language Uncertainty quantification framework.<n>ViLU constructs an uncertainty-aware multi-modal representation by integrating the visual embedding, the predicted textual embedding, and an image-conditioned textual representation via cross-attention.<n>Our proposed approach is well-suited for post-hoc settings, where only vision and text embeddings are available without direct access to the model itself.
• Score: 28.439422629957424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable Uncertainty Quantification (UQ) and failure prediction remain open challenges for Vision-Language Models (VLMs). We introduce ViLU, a new Vision-Language Uncertainty quantification framework that contextualizes uncertainty estimates by leveraging all task-relevant textual representations. ViLU constructs an uncertainty-aware multi-modal representation by integrating the visual embedding, the predicted textual embedding, and an image-conditioned textual representation via cross-attention. Unlike traditional UQ methods based on loss prediction, ViLU trains an uncertainty predictor as a binary classifier to distinguish correct from incorrect predictions using a weighted binary cross-entropy loss, making it loss-agnostic. In particular, our proposed approach is well-suited for post-hoc settings, where only vision and text embeddings are available without direct access to the model itself. Extensive experiments on diverse datasets show the significant gains of our method compared to state-of-the-art failure prediction methods. We apply our method to standard classification datasets, such as ImageNet-1k, as well as large-scale image-caption datasets like CC12M and LAION-400M. Ablation studies highlight the critical role of our architecture and training in achieving effective uncertainty quantification. Our code is publicly available and can be found here: https://github.com/ykrmm/ViLU.

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