3 accepted papers
Conventional reinforcement learning strategies for visual generation typically employ sample-wise reward functions, yet this practice frequently results in reward hacking that degrades image diversity and introduces visual anomalies. To address these limitations, we present a novel framework that fi…
3D occupancy perception accurately estimates the volumetric status and semantic labels of a scene, attracting significant attention in the field of autonomous driving. However, enhancing the model's ability to generalize across different driving scenarios or sensing systems, often requires redesigni…
High-quality data is crucial for the pre-training performance of large language models. Unfortunately, existing quality filtering methods rely on a known high-quality dataset as reference, which can introduce potential bias and compromise diversity. In this paper, we propose ScalingFilter, a novel a…