Visual concept discovery aims to extract compact and structured representations of the visual world, and recompose them to tackle novel intricate problems. It has played a crucial role in many core problems in computer vision research, including both discriminative and generative tasks. An important research question is to understand and design concept representations that facilitate better learning from various datasets and compositional reasoning. As an endeavor to answering this question, in this workshop, we gather together researchers in computer vision, multi-modal learning, machine learning, and cognitive science to discuss the following topics: 

1. Representations for learning computational models of visual concepts;

2. Objectives and sources of learning signals to facilitate visual concept learning, incorporating insights from various scientific fields including machine learning, natural language processing, and cognitive science;

3. Applications of visual concept learning and reasoning, including but not limited to visual scene understanding, robotics, and controllable image generation;

4. Interpretability of visual learning systems, delving deeper into how these systems learn, represent, and make use of learned concepts in various application domains.

We welcome submissions of short papers that tackle the above topics. Submissions should follow the CVPR format and be up to 4 pages, excluding references and supplementary material. The papers will not be published in proceedings. Accepted papers will be made publicly available as non-archival reports, allowing future submissions to archival conferences or journals. Authors will also present their work during poster sessions. Selected papers will be invited for spotlight presentations.

Submission deadline: April 8th, 2025

Acceptance notification date: April 29th, 2025

Camera-ready deadline: May 7th, 2025

Submission site: OpenReview

TBD

Please contact us to become a sponsor!