June 17, CVPR 2024 Workshop (Arch 210) & Posters #225 - 239
Current approaches in computer vision and machine learning primarily rely on identifying statistical correlations within massive datasets. This reliance limits their efficacy in areas that necessitate generalization through higher-order cognition, such as domain generalization and planning. A foundational approach to overcome these limitations involves incorporating principles of causality into the processing of large datasets. Similar to classic AI methodologies, causal inference usually assumes that the causal variables of interest are provided externally. However, real-world data, often encapsulated in high-dimensional, low-level observations (e.g., RGB pixels in a video), generally lacks organization into meaningful causal units.
This workshop aims to bring together researchers from structured (object-centric and causal) representation learning and robotics-oriented computer vision. To help integrate ideas from these areas, we invite researchers from Embodied AI, Causality and Representation Learning. We hope that this creates opportunities for discussion, presenting cutting-edge research, establishing new collaborations and identifying future research directions.
Object-centric and causal representation learning methods aim to overcome the challenges posed by conventional models that rely solely on correlations, offering a new pathway for advancing computer vision, particularly in dynamic and complex environments like robotics. Importantly, with the current vision and embodied AI systems, it is not obvious how to model interventions, counterfactuals, and hypotheticals without resorting to severe manual hand-engineering. While much can be done with significant supervision, ideally, robots and embodied agents should learn autonomously from the simulated environment. With those concepts in mind, we welcome contributions in the direction of:
For questions / comments, reach out to: corrworkshop@googlegroups.com
Website template adapted from the OSC/ORLR workshops, originally based on the template of the BAICS workshop.