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+---
+layout: publication
+title: "GIFT: A Framework for Global Interpretable Faithful Textual Explanations of Vision Classifiers" 
+image: assets/img/publications/2025_gift.PNG
+hide: false
+category: [explainability, driving, foundation]
+authors: Éloi Zablocki*, Valentin Gerard*, Amaia Cardiel, Eric Gaussier, Matthieu Cord, Eduardo Valle
+authors_internship: Éloi Zablocki*, <u>Valentin Gerard</u>*, Amaia Cardiel, Eric Gaussier, Matthieu Cord, Eduardo Valle
+venue: under review
+venue_long: under review
+year: 2025
+month: 6
+code_url: https://github.com/valeoai/GIFT 
+paper_url: 
+blog_url: 
+slides_url: 
+bib_url: 
+intern_work: true
+permalink: /publications/gift/
+---
+
+<h1 align="center"> {{page.title}} </h1>
+<!-- Simple call of authors -->
+<!-- <h3 align="center"> {{page.authors}} </h3> -->
+<!-- Alternatively you can add links to author pages -->
+<h3 align="center"> <a href="https://scholar.google.fr/citations?user=dOkbUmEAAAAJ">Éloi Zablocki</a> &nbsp;&nbsp; Valentin Gerard &nbsp;&nbsp; Amaia Cardiel &nbsp;&nbsp; <a href="https://ama.liglab.fr/~gaussier/">Eric Gaussier</a> &nbsp;&nbsp; <a href="https://cord.isir.upmc.fr/">Matthieu Cord</a> &nbsp;&nbsp; <a href="https://scholar.google.com/citations?user=lxWPqWAAAAAJ">Eduardo Valle</a></h3>
+
+
+<h3 align="center"> {{page.venue}} {{page.year}} </h3>
+
+<div align="center">
+  <p>
+    {% if page.paper_url %}
+    <a href="{{ page.paper_url }}"><i class="far fa-file-pdf"></i> Paper</a>&nbsp;&nbsp;
+    {% endif %}
+    {% if page.code_url %}
+    <a href="{{ page.code_url }}"><i class="fab fa-github"></i> Code</a> &nbsp;&nbsp;
+    {% endif %}
+    {% if page.blog_url %}
+    <a href="{{ page.blog_url }}"><i class="fab fa-blogger"></i> Blog</a> &nbsp;&nbsp;
+    {% endif %}
+    {% if page.slides_url %}
+    <a href="{{ page.slides_url }}"><i class="far fa-file-pdf"></i> Slides</a>&nbsp;&nbsp;
+    {% endif %}
+    {% if page.bib_url %}
+    <a href="{{ page.bib_url}}"><i class="far fa-file-alt"></i> BibTeX</a>&nbsp;&nbsp;
+    {% endif %}
+  </p>
+</div>
+
+
+<div class="publication-teaser">
+    <img src="../../{{ page.image }}" alt="project teaser"/>
+</div>
+
+
+<hr>
+
+<h2  align="center">Abstract</h2>
+
+<p align="justify">Understanding deep models is crucial for deploying them in safety-critical applications. We introduce GIFT, a framework for deriving post-hoc, global, interpretable, and faithful textual explanations for vision classifiers. GIFT starts from local faithful visual counterfactual explanations and employs (vision) language models to translate those into global textual explanations. Crucially, GIFT provides a verification stage measuring the causal effect of the proposed explanations on the classifier decision. Through experiments across diverse datasets, including CLEVR, CelebA, and BDD, we demonstrate that GIFT effectively reveals meaningful insights, uncovering tasks, concepts, and biases used by deep vision classifiers. Our code, data, and models are released at https://github.com/valeoai/GIFT.</p>
+
+<br>
+<hr>
+
+<h2  align="center">BibTeX</h2>
+<left>
+  <pre class="bibtex-box">
+@article{zablocki2025gift,
+  title     = {GIFT: A Framework for Global Interpretable Faithful Textual Explanations of Vision Classifiers},
+  author    = {{\'{E}}loi Zablocki and
+               Valentin Gerard and 
+               Amaia Cardiel and
+               Eric Gaussier and
+               Matthieu Cord and 
+               Eduardo Valle},
+  journal   = {arxiv},
+  year      = {2025}
+}
+</pre>
+</left>
+
+<br>