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 Neural Information Processing Systems




Image Understanding Makes for A Good Tokenizer for Image Generation Luting Wang Y ang Zhao

Neural Information Processing Systems

Modern image generation (IG) models have been shown to capture rich semantics valuable for image understanding (IU) tasks. However, the potential of IU models to improve IG performance remains uncharted. We address this issue using a token-based IG framework, which relies on effective tokenizers to map images into token sequences. Currently, pixel reconstruction (e.g., VQGAN) dominates the training objective for tokenizers. In contrast, our approach adopts the feature reconstruction objective, where tokenizers are trained by distilling knowledge from pretrained IU encoders. Comprehensive comparisons indicate that tokeniz-ers with strong IU capabilities achieve superior IG performance across a variety of metrics, datasets, tasks, and proposal networks.


A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems Yi Ma

Neural Information Processing Systems

To address this problem, existing methods partition the overall DPDP into fixed-size sub-problems by caching online generated orders and solve each sub-problem, or on this basis to utilize the predicted future orders to optimize each sub-problem further. However, the solution quality and efficiency of these methods are unsatisfactory, especially when the problem scale is very large.