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Workshops

154 Events
Workshop

AI for Content Creation

James Tompkin ⋅ Krishna Kumar Singh
Wed Half Day
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Workshop

LatinX in Computer Vision Research Workshop

Ana Maria Quintero ⋅ William de Lima
Wed Half Day
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Workshop

Workshop on "Bitter Lessons"

Anand Bhattad ⋅ Aditya Prakash
Wed Half Day
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Workshop

Workshop on Autonomous Driving

Vincent Casser ⋅ Jose M. Alvarez
Wed Full Day
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Workshop

Computational Cameras and Displays

Vishwanath Saragadam ⋅ Fei Xia
Wed Full Day
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Workshop

The 5th Explainable AI for Computer Vision (XAI4CV) Workshop

Miguel-Ángel Fernández-Torres
Wed Half Day
Computer vision for high-stakes, real-world applications necessitates robust explanation and transparency to ensure trust, accountability, and ethical deployment. Celebrating its 5th Anniversary, the Explainable AI for Computer Vision (XAI4CV) workshop provides a premier forum for the entire spectrum of XAI research, from interpretable-by-design models to challenges in multimodal foundational models. The program includes invited talks, spotlight papers, a poster session, and a tutorial. XAI4CV accepts paper and demo submissions to define the future of trustworthy visual AI.
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Workshop

Multimodal Alignment for a Pluralistic Society

Perampalli Shravan Nayak ⋅ Aishwarya Agrawal
Wed Half Day
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Workshop

Visual General Intelligence

Hirokatsu Kataoka ⋅ Yoshihiro Fukuhara
Wed Full Day
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Workshop

On Sensor Vision Workshop

Andrew J. Davison ⋅ Shinjeong Kim
Wed Half Day
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Workshop

Women in Computer Vision

Karen Sanchez ⋅ Carla Muntean
Wed Half Day
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Workshop

Auto-Annotation with Expert-Crafted Guidelines

Shu Kong ⋅ Sara Beery
Wed Half Day
Machine-learned visual systems are transforming numerous fields such as autonomous driving, biodiversity assessment, and ecological monitoring, but they hunger for vast, high-quality annotated data. Asking domain experts to manually annotate large-scale data is unrealistic; the current paradigm to scale up data annotation is to have domain experts craft annotation guidelines using visual examples and descriptions for non-expert annotators to apply. This paradigm is commonly adopted by companies which provide data labeling services. Lacking domain knowledge, ordinary annotators often produce annotations that are erroneous, subjective, biased, and inconsistent. Further, this process is labor-intensive, tedious, and costly. This workshop aims to pioneer auto-annotation, developing AI agents that can interpret expert-crafted annotation guidelines and generate labels automatically. In essence, we seek to replace ordinary human annotators with AI.
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Workshop

End-to-End 3D Learning

Zhiwen Fan ⋅ Dimitris Metaxas
Wed Half Day
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Workshop

2nd Workshop on Multimodal Sign Language Recognition

Raffaele Mineo ⋅ Hamzah Luqman
Wed Half Day
MSLR 2026 is the second edition of a rapidly growing venue on multimodal sign language recognition and translation. The program combines invited talks, a peer-reviewed track published in CVPR Workshops, and the SignEval Challenge featuring updated datasets for isolated LIS and continuous SLR. We emphasize privacy-preserving sensing (e.g., radar), healthcare accessibility, and inclusive practices with sign interpreters. Building on the success at ICCV 2025, MSLR 2026 will consolidate a global, interdisciplinary community spanning computer vision, linguistics, healthcare, and Deaf studies.
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Workshop

10th Affective & Behavior Analysis in-the-wild

Dimitrios Kollias ⋅ Panagiotis Tzirakis
Wed Half Day
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Workshop

Synthetic & Adversarial ForEnsics

Josué Martínez-Martínez ⋅ Pooya Khorrami
Wed Half Day
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Workshop

Sixth Workshop on Neural Architecture Search

Stephen McGough ⋅ Amir Atapour-Abarghouei
Wed Half Day
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Workshop

Machine Unlearning for Vision

Alessio Sampieri ⋅ Bardh Prenkaj
Wed Half Day
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Workshop

Spatial Intelligence for Cultural Heritage

Marina Paolanti ⋅ Roberto Pierdicca
Wed Half Day
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Workshop

Humans of Generative AI

Jaron Mink ⋅ David Forsyth
Thu Half Day
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Workshop

Open-World Vision

Shu Kong ⋅ Neehar Peri
Thu Half Day
Open-World Vision (OWV) emphasizes realistic opportunities and challenges in developing and deploying computer vision systems in the dynamic, vast, and unpredictable real open world, which offers abundant data that can benefit training and challenge testing. It contrasts the traditional "closed-world" paradigm of visual learning and inference, which assumes fixed, known data distributions and categorical labels. Models developed under such closed-world assumptions tend to be brittle when encountering ever-changing and novel scenarios in the real open world. Modern visual learning has shifted towards an open-world paradigm, such as pretraining foundation models on massive data sourced from the open world (e.g., web-sourced data). While these models show unprecedented performance and strong adaptability to downstream tasks, they inherit biases from their open-world pretraining data and can still fail in truly novel or underrepresented scenarios during deployment. This workshop aims not only to uncover current limitations, potential risks, emerging opportunities, and unresolved challenges of open-world vision, but also to solicit solutions that advance the field toward more robust, fair, and adaptable visual systems.
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Workshop

How Do Vision Models Work?

Tamar Rott Shaham ⋅ Amil Dravid
Thu Full Day
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Workshop

Sight and Sound

Andrew Owens ⋅ Jiajun Wu
Thu Full Day
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Workshop

Safe Artificial Intelligence for All Domains

Oliver Wasenmüller ⋅ Markus Enzweiler
Thu Half Day
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Workshop

Artificial Intelligence for Space

Daniele Gammelli ⋅ Gabriele Meoni
Thu Half Day
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Workshop

Second Workshop on Skilled Activity Understanding, Assessment & Feedback Generation

Paritosh Parmar ⋅ Brendan Morris
Thu Half Day
Imagine a world where computer vision-based systems can analyze a video of an athlete, a surgeon, a patient, or a factory worker and instantly provide expert-level actionable feedback---correcting techniques, identifying inefficiencies, and helping people refine their skills in real time. Thanks to rapid progress in video understanding, this vision is becoming reality. AI-powered systems can now analyze complex human activities, assess performance, and generate intelligent feedback, unlocking new possibilities in sports, healthcare, manufacturing, education, rehabilitation, and beyond. Through Expert Keynotes and Invited Contributions, this CVPR 2026 workshop will explore the cutting edge of skilled activity understanding, assessment, and feedback generation, bridging research and real-world applications.

As AI systems become more capable of understanding human expertise, the implications are profound---empowering individuals with personalized coaching, democratized skill development, and scalable training solutions. We invite researchers, industry leaders, and practitioners to join us in shaping the future of AI-powered skill understanding. Whether working on foundational research, applied solutions, or real-world deployment, this workshop is an opportunity and forum to learn about and push the boundaries of how AI perceives, evaluates, and enhances human ability.
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