BoxeR: Box-Attention for 2D and 3D Transformers
In this paper, we propose a simple attention mechanism, we call Box-Attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves the learning capability of transformers for several vision tasks. Specifically, we present BoxeR, short for Box Transfo...
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| Vydané v: | Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 4763 - 4772 |
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| Hlavní autori: | , , , , |
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| Jazyk: | English |
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01.06.2022
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| ISSN: | 1063-6919 |
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| Abstract | In this paper, we propose a simple attention mechanism, we call Box-Attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves the learning capability of transformers for several vision tasks. Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map. The BoxeR computes attention weights on these boxes by considering its grid structure. Notably, BoxeR-2D naturally reasons about box information within its attention module, making it suitable for end-to-end instance detection and segmentation tasks. By learning invariance to rotation in the box-attention module, BoxeR-3D is capable of generating discriminative information from a bird's-eye view plane for 3D end-to-end object detection. Our experiments demonstrate that the proposed BoxeR-2D achieves state-of-the-art results on COCO detection and instance segmentation. Besides, BoxeR-3D improves over the end-to-end 3D object detection baseline and already obtains a compelling performance for the vehicle category of Waymo Open, without any class-specific optimization. Code is available at https://github.com/kienduynguyen/BoxeR. |
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| AbstractList | In this paper, we propose a simple attention mechanism, we call Box-Attention. It enables spatial interaction between grid features, as sampled from boxes of interest, and improves the learning capability of transformers for several vision tasks. Specifically, we present BoxeR, short for Box Transformer, which attends to a set of boxes by predicting their transformation from a reference window on an input feature map. The BoxeR computes attention weights on these boxes by considering its grid structure. Notably, BoxeR-2D naturally reasons about box information within its attention module, making it suitable for end-to-end instance detection and segmentation tasks. By learning invariance to rotation in the box-attention module, BoxeR-3D is capable of generating discriminative information from a bird's-eye view plane for 3D end-to-end object detection. Our experiments demonstrate that the proposed BoxeR-2D achieves state-of-the-art results on COCO detection and instance segmentation. Besides, BoxeR-3D improves over the end-to-end 3D object detection baseline and already obtains a compelling performance for the vehicle category of Waymo Open, without any class-specific optimization. Code is available at https://github.com/kienduynguyen/BoxeR. |
| Author | Nguyen, Duy-Kien Snoek, Cees G. M. Oswald, Martin R. Ju, Jihong Booij, Olaf |
| Author_xml | – sequence: 1 givenname: Duy-Kien surname: Nguyen fullname: Nguyen, Duy-Kien email: d.k.nguyen@uva.nl organization: Atlas Lab-University of Amsterdam – sequence: 2 givenname: Jihong surname: Ju fullname: Ju, Jihong email: jihong.ju@tomtom.com organization: TomTom – sequence: 3 givenname: Olaf surname: Booij fullname: Booij, Olaf email: olaf.booij@tomtom.com organization: TomTom – sequence: 4 givenname: Martin R. surname: Oswald fullname: Oswald, Martin R. email: m.r.oswald@uva.nl organization: Atlas Lab-University of Amsterdam – sequence: 5 givenname: Cees G. M. surname: Snoek fullname: Snoek, Cees G. M. email: cgmsnoek@uva.nl organization: Atlas Lab-University of Amsterdam |
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| Snippet | In this paper, we propose a simple attention mechanism, we call Box-Attention. It enables spatial interaction between grid features, as sampled from boxes of... |
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| SubjectTerms | categorization Codes Computer vision grouping and shape analysis Object detection Pattern recognition Recognition: detection retrieval; Deep learning architectures and techniques; Segmentation Task analysis Three-dimensional displays Transformers |
| Title | BoxeR: Box-Attention for 2D and 3D Transformers |
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