What is CV-MIM?
CV-MIM stands for Contrastive Cross-View Mutual Information Maximization. This is a method that is used for representation learning, specifically to disentangle view-dependent factors and pose-dependent factors. Its main aim is to maximize the mutual information between the same pose as viewed from different viewpoints, using a contrastive learning mechanism.
How Does CV-MIM Work?
CV-MIM works by training a network to learn features that are relevant to a particular pose. The
What is VideoBERT?
VideoBERT is a machine learning model that is used to learn a joint visual-linguistic representation for video. It is adapted from the powerful BERT model, which was originally developed for natural language processing. VideoBERT is capable of performing a variety of tasks related to video, including action classification and video captioning.
How does VideoBERT work?
VideoBERT works by encoding both video frames and textual descriptions of those frames into a joint embedd
Vision-and-Language BERT, also known as ViLBERT, is an innovative model that combines both natural language and image content to learn task-agnostic joint representations. This model is based on the popular BERT architecture and expands it into a multi-modal two-stream model that processes both visual and textual inputs. What sets ViLBERT apart from other models is its ability to interact through co-attentional transformer layers, making it highly versatile and useful for various applications.