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arXiv · 2501.08303

Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers

Efstathios Karypidis·Ioannis Kakogeorgiou·Spyros Gidaris·Nikos Komodakis
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Abstract

Semantic future prediction is important for autonomous systems navigating dynamic environments. This paper introduces FUTURIST, a method for multimodal future semantic prediction that uses a unified and efficient visual sequence transformer architecture. Our approach incorporates a multimodal masked visual modeling objective and a novel masking mechanism designed for multimodal training. This allows the model to effectively integrate visible information from various modalities, improving prediction accuracy. Additionally, we propose a VAE-free hierarchical tokenization process, which reduces computational complexity, streamlines the training pipeline, and enables end-to-end training with high-resolution, multimodal inputs. We validate FUTURIST on the Cityscapes dataset, demonstrating state-of-the-art performance in future semantic segmentation for both short- and mid-term forecasting. Project page and code at https://futurist-cvpr2025.github.io/ .

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Efstathios Karypidis, Ioannis Kakogeorgiou, Spyros Gidaris, Nikos Komodakis. 2025-01-14. Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers. https://arxiv.org/abs/2501.08303

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