Multimodal image fusion and object detection are crucial for autonomous driving. While current methods have advanced the fusion of texture details and semantic information, their complex training processes hinder broader applications. Addressing this challenge, we introduce E2E-MFD, a novel end-to-end algorithm for multimodal fusion detection.

Neural Information Processing Systems http://nips.cc/

Deep neural networks are notorious for being sensitive to small well-chosen perturbations, and estimating the regularity of such architectures is of utmost importance for safe and robust practical applications. In this paper, we investigate one of the key characteristics to assess the regularity of such methods: the Lipschitz constant of deep learning architectures.

One emerging area of research is the fast adaptation of large text-to-image models to smaller datasets or new visual concepts.

Neural Information Processing Systems http://nips.cc/

Neural Information Processing Systems http://nips.cc/