Audiovisual SlowFast Network or AVSlowFast is an innovative architecture that aims to unite visual and audio modalities in a single, integrated perception. The Slow and Fast visual pathways of the network, fused with a Faster Audio pathway, work together to model the combined effect of vision and sound. In this way, AVSlowFast creates a comprehensive and authentic representation of how sight and hearing combine in human experiences.
Integrating Audio and Visual Features
AVSlowFast was designe
Overview of CTAL: Pre-Training Framework for Audio-and-Language Representations
CTAL is a pre-training framework for creating strong audio-and-language representations with a Transformer. In simpler terms, it helps computers understand the relationship between spoken language and written language.
How does CTAL work?
CTAL accomplishes its goal through two types of tasks that it performs on a large number of audio and language pairs: masked language modeling and masked cross-modal acoustic mo
Are you looking for a way to generate photorealistic images based on text descriptions? Then look no further than GLIDE, a cutting-edge generative model that uses text-guided diffusion models to create stunning images.
What is GLIDE?
GLIDE is a powerful image generation model that is built on text-guided diffusion models. Essentially, this means that you can give GLIDE a natural language prompt, and it will use a diffusion model to create a highly detailed and photorealistic image based on th
What is MAVL?
MAVL stands for Multiscale Attention ViT with Late fusion. It is a multi-modal neural network that is trained to detect objects using human understandable natural language text queries. The network uses multiple image features and deforms the convolution for late multi-modal fusion.
What does MAVL do?
MAVL is a class-agnostic object detector that can be used to identify objects in an image. It uses natural language text queries, such as "all objects" or "all entities," to detec
What is UNIMO?
UNIMO is a pre-training architecture that can adapt to both single modal and multimodal understanding and generation tasks. Essentially, UNIMO can understand and create meaning from both text and visual representations. It does this by learning both types of representations simultaneously and then aligning them into the same semantic space based on image-text pairs.
How does UNIMO work?
UNIMO is based on a cross-modal contrastive learning approach. This means that it learns by
Overview of Video-Audio-Text Transformer (VATT)
Video-Audio-Text Transformer, also known as VATT, is a framework for learning multimodal representations from unlabeled data. VATT is unique because it uses convolution-free Transformer architectures to extract multidimensional representations that are rich enough to benefit a variety of downstream tasks. This means that VATT takes raw signals, such as video, audio, and text, as inputs and creates representations that can be used for many differen
Vokenization is an emerging approach for linking language with visual elements based on contextual mapping. Simply put, vokens are images or pictures that have been mapped to specific language tokens in order to provide a more comprehensive understanding of language. This process of mapping is done through a retrieval mechanism that links language and images together.
How Does Vokenization Work?
Vokenization works by retrieving images that are related to specific language tokens in order to p