Creating Hiera A Simple and Accurate Hierarchical Vision Transformer

Can a simpler hierarchical vision transformer be more accurate than complex ones? Hiera, a new model, claims to be just that.

Creating Hiera A Simple and Accurate Hierarchical Vision Transformer

The field of computer vision and pattern recognition has seen significant advancements in recent years, with the introduction of hierarchical vision transformers. These models have been designed to improve supervised classification performance by incorporating several vision-specific components. While these additions have led to improved accuracy and reduced FLOP counts, they have also resulted in increased complexity and slower processing times. A new paper argues that this additional bulk is unnecessary and presents a model called Hiera, which is a simple hierarchical vision transformer that outperforms previous models while being significantly faster.

The authors of the paper propose pretraining Hiera with a strong visual pretext task called MAE, which allows them to strip out all the bells-and-whistles from the state-of-the-art multi-stage vision transformer without sacrificing accuracy. By doing so, they create a model that is both simpler and more efficient than its predecessors. The researchers evaluate Hiera on various tasks for image and video recognition and demonstrate its superior performance over other hierarchical vision transformers.

One of the key advantages of Hiera is its simplicity. Unlike other hierarchical vision transformers, it does not require any additional components or complex architectures to achieve high accuracy levels. This makes it easier to understand and implement, making it an attractive option for researchers who want to develop new computer vision applications quickly.

Another advantage of Hiera is its speed. Because it has fewer components than other hierarchical vision transformers, it can process data more quickly both during training and inference stages. This makes it well-suited for real-time applications where fast processing times are critical.

The code and models used in the study are available online at a specified URL, making it easy for other researchers to replicate their results or build upon their work. This open-source approach is important in advancing the field of computer vision as it allows others to build on existing research rather than starting from scratch.

The paper demonstrates the potential of pretraining models with strong visual pretext tasks to simplify and improve the efficiency of hierarchical vision transformers. While Hiera is a significant improvement over previous models, it is likely that further research will continue to refine and improve these models, leading to even more efficient and accurate computer vision applications in the future.