In this paper, we investigate integrating this inductive bias of sparse interactions into thelatent dynamics ofworld models trained from pixels.

Wefurther evaluated state-of-the-art models on this benchmark forthree vision-language tasks: image captioning, visual grounding, and visual question answering. Our work aims to significantly contribute to the development ofadvanced vision-language models inthefieldofremote sensing.

Neural architecture search (NAS) has demonstrated impressive performance in automatically designing high-performance neural networks. The power ofdeep neural networks is to be unleashed for analyzing a large volume of data (e.g.

Inoursetup,armsareassociated with fixed costs and are ordered, forming acascade. In each round, acontext is presented, andthelearner selects thearmssequentially tillsome depth.