While Deep Neural Networks (DNNs) exceed at predictive performance, they are often too complex to be understood by humans, rendering them “closed-box models”, which is of particular concern when applied in safety-critical domains such as autonomous driving or medical applications. Therefore, explainable artificial intelligence (XAI) aims to better understand DNNs, ultimately leading to more robust, fair, and interpretable models. While this important field of research has been gaining traction, there is also justified criticism of the way in which the research is conducted. For example, the term “explainability” in itself is not consistently defined and is highly dependent on the end user and task, leading to ill-defined research questions and a lack of standardized evaluation practices. Motivated by this, the goal of our workshop series is the discussion and dissemination of ideas at the cutting-edge of XAI research, while also introducing a dedicated sub-theme each year to encourage critical introspection on the challenges faced by the community. To this end, we have dedicated paper tracks (proceedings, non-proceedings), a poster session, elevator pitches for each poster, invited speakers presenting their latest work, and a panel discussion focusing on theme-specific challenges. In this third iteration of our workshop, our theme will be challenges to and opportunities for XAI in the era of large foundation models, which have been rapidly gaining traction in the field of computer vision and beyond. This focus will be reflected in the selection of the invited speakers—we aim for research expertise at the intersection of traditional XAI and foundation models—and, especially, in the composition and framing of the panel discussion.
The rapid rise of foundation models over the past few years has made the role of XAI even more critical. For example, the widespread deployment of generative AI models has given them the power to steer collective thought processes, shift cultural norms, and spread biases. Thus, it is essential to have a proper understanding of their inner workings, the knowledge they store, and how to steer them away from harmful and biased outputs. However, they have also brought forth fresh challenges to the XAI field such as handling scale, maintaining model performance, performing appropriate evaluations, complying with regulatory requirements, and fundamentally rethinking what one wants from an explanation. Traditional XAI methods can often not be directly applied to these models, and even when they are, it is not clear whether they are still useful. Thus, the field of XAI needs to adapt to the rapidly changing landscape of AI research and deployment, and this workshop will act as a platform to foster discussion on how to do so. A key goal for our workshop this year is to bring together researchers who have been working on classical XAI methods from before foundation models and those who have been at the forefront of XAI for such models, to foster a fruitful exchange of ideas and perspectives.
The eXCV workshop aims to advance and critically examine the current landscape in the field of XAI for computer vision. To this end, we invite papers covering all topics within XAI for computer vision, including but not limited to:
Since the aim of the workshop is not only to present new XAI methods but also to question current practices, we also invite papers that present interesting and detailed negative results and papers that show the limitations of today's XAI methods. In particular, we invite papers that explore this year's theme: the challenges and opportunities emerging in the era of foundation models.
The workshop has three submission tracks. For all tracks, accepted papers will be presented in-person in the poster session of the workshop. At least one author for each accepted paper should plan to attend the workshop to present a poster.
Each submission must designate at least one reciprocal reviewer. Every reciprocal reviewer will be assigned up to three papers and may serve in this role for no more than three submissions. Reviewing is double-blind.
Papers in these tracks will be published in the ECCV 2026 Workshop Proceedings, and must be up to 14 pages in length excluding references and supplementary material. Papers submitted to the Proceedings Track should follow the ECCV 2026 Submission Policies and the Author Guidelines. Each accepted paper in the Proceedings Track must follow the ECCV 2026 guidelines (e.g. for Author Registration). Please see the ECCV 2026 Registration page for the most up to date details.
Papers in this track will not be published in the ECCV 2026 Workshop Proceedings. We invite papers that have been previously published at a leading international conference on computer vision or machine learning in 2025 or 2026 (e.g., ECCV, ICCV, CVPR, NeurIPS, ICLR, ICML, AAAI). The aim of the Nectar Track is to increase the visibility of exciting XAI work and to give researchers an opportunity to connect with the XAI community. The submission should be a single PDF containing the already published paper (not anonymized and in the formatting of the original venue).
Proceedings Tracks:
Non-Proceedings (Nectar) Track:
