Deep neural models, in recent years, have been successful in almost every field, even
solving the most complex problem statements. However, these models are huge in size with …

As camera and LiDAR sensors capture complementary information in autonomous driving,
great efforts have been made to conduct semantic segmentation through multi-modality data …

In recent years, deep neural networks have been successful in both industry and academia,
especially for computer vision tasks. The great success of deep learning is mainly due to its …

Abstract Domain generalization aims to learn a generalizable model from aknown'source
domain for variousunknown'target domains. It has been studied widely by domain …

We are concerned with a worst-case scenario in model generalization, in the sense that a
model aims to perform well on many unseen domains while there is only one single domain …

Image virtual try-on aims to fit a garment image (target clothes) to a person image. Prior
methods are heavily based on human parsing. However, slightly-wrong segmentation …

A common assumption in multimodal learning is the completeness of training data, ie, full
modalities are available in all training examples. Although there exists research endeavor in …

Visual localization is a fundamental task for various applications including autonomous
driving and robotics. Prior methods focus on extracting large amounts of often redundant …

Adversarial data augmentation has shown promise for training robust deep neural networks
against unforeseen data shifts or corruptions. However, it is difficult to define heuristics to …

Stereo-based 3D detection aims at detecting 3D object bounding boxes from stereo images
using intermediate depth maps or implicit 3D geometry representations, which provides a …