Heatmap regression has dominated human pose estimation due to its superior performance and strong generalization.

This paper presents a new mechanism to facilitate the training of mask transformers for efficient panoptic segmentation, democratizing its deployment. We observe that due to the high complexity in the training objective of panoptic segmentation, it will inevitably lead to much higher penalization on false positive.

They live in the same physical world and are intimately familiar with the materials that comprise it, but they would have significant difficulty expressing their values and generalizing the results of an experiment they observetogether. The alchemist would likely learn poorly from examples of a reaction demonstrated by the chemist, not having the right inductive biases for the waytheworldactuallyworks.

This dual approach bridges the semantic gap between visual and textual data, thereby improving the model's ability to process and interpret information from multiple images effectively.

Preference learning algorithms (e.g., RLHF and DPO) are frequently used to steer LLMs to produce generations that are more preferred by humans, but our understanding of their inner workings is still limited.

Part of this work was done during Kang Xu's internship at Shanghai Artificial Intelligence Laboratory.