This paper presents Jigsaw, a novel framework for assembling physically broken 3D objects from multiple pieces.

For what purpose was the dataset created? We created this CityRefer dataset to facilitate research toward city-scale 3D visual grounding. Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., Who funded the creation of the dataset? What do the instances that comprise the dataset represent? CityRefer contains descriptions for 3D visual grounding on large-scale point cloud data.

We present an alternative approach to decompose non-negative tensors, called many-body approximation .

The pre-trained Large Language Models (LLMs) can be adapted for many downstream tasks and tailored to align with human preferences through fine-tuning.

This paper contains uncensored toxic content that might be offensive. Toxicity classification in textual content remains a significant problem.

We present a novel approach for test-time adaptation via online self-training, consisting of two components.

CIFAR-10-L T, CIFAR-100-L T, ImageNet-100-L T, and Places-L T are 5, 80, 50, and 182 respectively. Our default training set of each dataset is summarized in Table 8.