MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

In this paper, we study Multiscale Vision Transformers (MViTv2) as a unified architecture for image and video classification, as well as object detection. Without bells-and-whistles, MViTv2 has advanced performance in 3 domains: 88.8% accuracy on ImageNet classification, 58.7 AP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">box</sup> on COCO object detection as well as 86.1% on Kinetics-400 video classification.

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• 1 We present an improved version of MViT that incorporates decomposed relative positional embeddings and residual pooling connections.
• 2 We instantiate this architecture in five sizes and evaluate it for ImageNet classification, COCO detection and Kinetics video recognition where it outperforms prior work.
• 3 We further compare MViTv2s' pooling attention to window attention mechanisms where it outperforms the latter in accuracy/compute.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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