What question did this study set out to answer?

To alleviate the severe computational and memory burdens of pairwise token interactions in vision transformers without relying on rigid, content-agnostic sparsity patterns.

June 1, 2023

BiFormer: Vision Transformer with Bi-Level Routing Attention

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Authors

LZLei Zhu XWXinjiang Wang ZKZhanghan Ke

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Overview

Benchmarking study demonstrates improved computational efficiency in dense visual prediction tasks, highlighting the benefits of query-adaptive sparse attention routing.

Key Points

To alleviate the severe computational and memory burdens of pairwise token interactions in vision transformers without relying on rigid, content-agnostic sparsity patterns.
Designed a dynamic sparse attention mechanism using bi-level routing to filter out irrelevant key-value pairs across coarse regions before applying fine-grained token-to-token attention.
Implemented the routing mechanism using hardware-friendly dense matrix multiplications to maximize operational speed on GPUs.
Constructed the BiFormer general vision transformer and evaluated its performance on benchmark tasks including image classification, object detection, and semantic segmentation.
Bi-level routing attention successfully directed computational focus to small subsets of task-relevant tokens while disregarding distracting background regions.
BiFormer achieved superior performance trade-offs with high computational efficiency and reduced memory consumption, particularly across dense visual prediction benchmarks.

Cite This Study

Zhu et al. (2023) studied this question.

synapsesocial.com/papers/69d72bfe5dca7d66cbbef135 https://doi.org/10.1109/cvpr52729.2023.00995

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June 1, 2023

BiFormer: Vision Transformer with Bi-Level Routing Attention

View Full Paper

Ask AI

Bookmark

Authors

LZLei Zhu XWXinjiang Wang ZKZhanghan Ke

Discussion

Member takes

Overview

Benchmarking study demonstrates improved computational efficiency in dense visual prediction tasks, highlighting the benefits of query-adaptive sparse attention routing.

Key Points

To alleviate the severe computational and memory burdens of pairwise token interactions in vision transformers without relying on rigid, content-agnostic sparsity patterns.
Designed a dynamic sparse attention mechanism using bi-level routing to filter out irrelevant key-value pairs across coarse regions before applying fine-grained token-to-token attention.
Implemented the routing mechanism using hardware-friendly dense matrix multiplications to maximize operational speed on GPUs.
Constructed the BiFormer general vision transformer and evaluated its performance on benchmark tasks including image classification, object detection, and semantic segmentation.
Bi-level routing attention successfully directed computational focus to small subsets of task-relevant tokens while disregarding distracting background regions.
BiFormer achieved superior performance trade-offs with high computational efficiency and reduced memory consumption, particularly across dense visual prediction benchmarks.

Cite This Study

Zhu et al. (2023) studied this question.

synapsesocial.com/papers/69d72bfe5dca7d66cbbef135 https://doi.org/10.1109/cvpr52729.2023.00995

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