Papers › DAT++: Spatially Dynamic Vision Transformer with Deformable Attention

DAT++: Spatially Dynamic Vision Transformer with Deformable Attention

4 Sep 2023arXiv:2309.01430archive 2025-07-28

Zhuofan Xia, Xuran Pan, Shiji Song, Li Erran Li, Gao Huang

Transformers have shown superior performance on various vision tasks. Their large receptive field endows Transformer models with higher representation power than their CNN counterparts. Nevertheless, simply enlarging the receptive field also raises several concerns. On the one hand, using dense attention in ViT leads to excessive memory and computational cost, and features can be influenced by irrelevant parts that are beyond the region of interests. On the other hand, the handcrafted attention adopted in PVT or Swin Transformer is data agnostic and may limit the ability to model long-range relations. To solve this dilemma, we propose a novel deformable multi-head attention module, where the positions of key and value pairs in self-attention are adaptively allocated in a data-dependent way. This flexible scheme enables the proposed deformable attention to dynamically focus on relevant regions while maintains the representation power of global attention. On this basis, we present Deformable Attention Transformer (DAT), a general vision backbone efficient and effective for visual recognition. We further build an enhanced version DAT++. Extensive experiments show that our DAT++ achieves state-of-the-art results on various visual recognition benchmarks, with 85.9% ImageNet accuracy, 54.5 and 47.0 MS-COCO instance segmentation mAP, and 51.5 ADE20K semantic segmentation mIoU.

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Tasks

Image ClassificationInstance SegmentationObject DetectionSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Image Classification ImageNet DAT-B++ (384x384) GFLOPs 49.7 #187 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-B++ (384x384) Number of params 94M #187 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-B++ (384x384) Top 1 Accuracy 85.9% #187 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-B++ (224x224) GFLOPs 16.6 #280 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-B++ (224x224) Number of params 93M #280 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-B++ (224x224) Top 1 Accuracy 84.9% #280 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-S++ GFLOPs 9.4 #302 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-S++ Number of params 53M #302 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-S++ Top 1 Accuracy 84.6% #302 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-T++ GFLOPs 4.3 #377 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-T++ Number of params 24M #377 of 1060 Archive leaderboard report
Image Classification ImageNet DAT-T++ Top 1 Accuracy 83.9% #377 of 1060 Archive leaderboard report
Object Detection COCO 2017 DAT-S++ AP 50.2 #4 of 24 Archive leaderboard report
Object Detection COCO 2017 DAT-T++ AP 49.2 #5 of 24 Archive leaderboard report
Semantic Segmentation ADE20K DAT-B++ Validation mIoU 51.5 #96 of 235 Archive leaderboard report
Semantic Segmentation ADE20K DAT-S++ Validation mIoU 51.2 #99 of 235 Archive leaderboard report
Semantic Segmentation ADE20K DAT-T++ Validation mIoU 50.3 #115 of 235 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutFocusLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPVTPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxSpatial-Reduction AttentionStochastic DepthSwin TransformerTransformer

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