18476037. Channel Fusion for Vision-Language Representation Learning simplified abstract (GOOGLE LLC)

Channel Fusion for Vision-Language Representation Learning

Organization Name

GOOGLE LLC

Inventor(s)

Anthony J. Piergiovanni of Denver CO (US)

Maxwell Mbabilla Aladago of Hanover NH (US)

Channel Fusion for Vision-Language Representation Learning - A simplified explanation of the abstract

This abstract first appeared for US patent application 18476037 titled 'Channel Fusion for Vision-Language Representation Learning

Simplified Explanation

The approach described in the abstract aligns multi-modal tokens using cross-attention while maintaining the benefits of global self-attention. Instead of concatenating unimodal tokens along the sequence dimension, this approach aligns per-modality tokens by chaining them along the channels. This allows tokens from one modality to query the other modality, with the output concatenated with the query tokens on the channels. This process can be repeated or performed in parallel with the roles of the modalities switched. The resulting compound tokens can then be fed into a self-attention encoder like a transformer encoder.

• Multi-modal tokens aligned using cross-attention
• Per-modality tokens chained along channels for alignment
• Query tokens from one modality used to query the other modality
• Output concatenated with query tokens on channels
• Process can be repeated or performed in parallel with roles of modalities switched
• Compound tokens fed into self-attention encoder like transformer encoder

Potential Applications

The technology could be applied in various fields such as natural language processing, computer vision, and speech recognition to improve multi-modal data processing and understanding.

Problems Solved

This technology addresses the challenge of effectively aligning multi-modal tokens without losing the advantages of global self-attention, enabling better integration of information from different modalities.

Benefits

- Enhanced alignment of multi-modal tokens - Improved information integration across different modalities - Retention of benefits of global self-attention

Potential Commercial Applications

"Enhancing Multi-Modal Token Alignment Using Cross-Attention" in Various Industries

Possible Prior Art

There may be prior art related to multi-modal token alignment techniques using attention mechanisms in the fields of natural language processing and computer vision.

How does this approach compare to existing methods of multi-modal token alignment?

This article does not provide a direct comparison to existing methods of multi-modal token alignment, leaving the reader to infer the advantages and disadvantages based on the description provided.

What specific improvements in performance can be expected from implementing this approach?

The article does not delve into the specific performance improvements that can be expected from implementing this approach, leaving room for further exploration and experimentation in this area.

Original Abstract Submitted

Provided is an approach that aligns multi-modal tokens using cross-attention without losing the advantages of global self-attention. In contrast to previous works that concatenate the unimodal tokens along the sequence dimension, example approaches described herein align per-modality tokens by chaining them along the channels. Specifically, the tokens from one modality can be used to query the other modality and the output can be concatenated with the query tokens on the channels. An analogous process can also be repeated (or performed in parallel) where the roles of the two modalities are switched. The resulting sets of compound tokens can be concatenated and fed into a self-attention encoder such as a transformer encoder that performs self-attention.