We focus on scenarios where data distributions vary across multiple segments of the entire population and only make local assumptions about the differences between training and test (deployment) distributions within each segment.
Large language models have exhibited robust performance across diverse natural language processing tasks.
In this paper, we showcase the effectiveness of optimizing monocular camera poses as a continuous function of time.
We believe that the combination is complementary and able to solve the inherent difficulties of using one modality input, including occlusions, extreme lighting/texture, and out-of-view for visual mocap and global drifts for inertial mocap.
Ranked #1 on 3D Human Pose Estimation on AIST++
In this paper, we propose an understanding-oriented machine reading comprehension model to address three kinds of robustness issues, which are over sensitivity, over stability and generalization.
When DSA module and object confidence task are applied in RetinaNet together, the detection performances based on ResNet50 and ResNet101 can be increased by 1. 0% AP and 1. 4% AP respectively.
Estimating parameters of mixture model has wide applications ranging from classification problems to estimating of complex distributions.