In the past few years, the emergence of pre-training models has brought uni-modal fields such as computer vision (CV) and natural language processing (NLP) to a new era …
P Xu, X Zhu, DA Clifton - IEEE Transactions on Pattern Analysis …, 2023 - ieeexplore.ieee.org
Transformer is a promising neural network learner, and has achieved great success in various machine learning tasks. Thanks to the recent prevalence of multimodal applications …
How should we integrate representations from complementary sensors for autonomous driving? Geometry-based fusion has shown promise for perception (eg, object detection …
Vision-language representation learning largely benefits from image-text alignment through contrastive losses (eg, InfoNCE loss). The success of this alignment strategy is attributed to …
Unsupervised large-scale vision-language pre-training has shown promising advances on various downstream tasks. Existing methods often model the cross-modal interaction either …
How should representations from complementary sensors be integrated for autonomous driving? Geometry-based sensor fusion has shown great promise for perception tasks such …
A Radford, JW Kim, C Hallacy… - International …, 2021 - proceedings.mlr.press
State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories. This restricted form of supervision limits their generality and usability since …
This paper presents a simple yet effective framework MaskCLIP, which incorporates a newly proposed masked self-distillation into contrastive language-image pretraining. The core idea …
Large-scale pre-trained models (PTMs) such as BERT and GPT have recently achieved great success and become a milestone in the field of artificial intelligence (AI). Owing to …