LoFLAT: Local Feature Matching using Focused Linear Attention Transformer

• URL: http://arxiv.org/abs/2410.22710v1
• Date: Wed, 30 Oct 2024 05:38:07 GMT
• Title: LoFLAT: Local Feature Matching using Focused Linear Attention Transformer
• Authors: Naijian Cao, Renjie He, Yuchao Dai, Mingyi He,
• Abstract summary: We propose the LoFLAT, a novel Local Feature matching using Focused Linear Attention Transformer. Our LoFLAT consists of three main modules: the Feature Extraction Module, the Feature Transformer Module, and the Matching Module. The proposed LoFLAT outperforms the LoFTR method in terms of both efficiency and accuracy.
• Score: 36.53651224633837
• License:
• Abstract: Local feature matching is an essential technique in image matching and plays a critical role in a wide range of vision-based applications. However, existing Transformer-based detector-free local feature matching methods encounter challenges due to the quadratic computational complexity of attention mechanisms, especially at high resolutions. However, while existing Transformer-based detector-free local feature matching methods have reduced computational costs using linear attention mechanisms, they still struggle to capture detailed local interactions, which affects the accuracy and robustness of precise local correspondences. In order to enhance representations of attention mechanisms while preserving low computational complexity, we propose the LoFLAT, a novel Local Feature matching using Focused Linear Attention Transformer in this paper. Our LoFLAT consists of three main modules: the Feature Extraction Module, the Feature Transformer Module, and the Matching Module. Specifically, the Feature Extraction Module firstly uses ResNet and a Feature Pyramid Network to extract hierarchical features. The Feature Transformer Module further employs the Focused Linear Attention to refine attention distribution with a focused mapping function and to enhance feature diversity with a depth-wise convolution. Finally, the Matching Module predicts accurate and robust matches through a coarse-to-fine strategy. Extensive experimental evaluations demonstrate that the proposed LoFLAT outperforms the LoFTR method in terms of both efficiency and accuracy.

Related papers

• PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection [59.355022416218624]
  integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
  arXiv  Detail & Related papers  (2024-08-26T19:43:01Z)
• PointMT: Efficient Point Cloud Analysis with Hybrid MLP-Transformer Architecture [46.266960248570086]
  This study tackles the quadratic complexity of the self-attention mechanism by introducing a complexity local attention mechanism for effective feature aggregation. We also introduce a parameter-free channel temperature adaptation mechanism that adaptively adjusts the attention weight distribution in each channel. We show that PointMT achieves performance comparable to state-of-the-art methods while maintaining an optimal balance between performance and accuracy.
  arXiv  Detail & Related papers  (2024-08-10T10:16:03Z)
• Deformable Mixer Transformer with Gating for Multi-Task Learning of Dense Prediction [126.34551436845133]
  CNNs and Transformers have their own advantages and both have been widely used for dense prediction in multi-task learning (MTL) We present a novel MTL model by combining both merits of deformable CNN and query-based Transformer with shared gating for multi-task learning of dense prediction.
  arXiv  Detail & Related papers  (2023-08-10T17:37:49Z)
• FLatten Transformer: Vision Transformer using Focused Linear Attention [80.61335173752146]
  Linear attention offers a much more efficient alternative with its linear complexity. Current linear attention approaches either suffer from significant performance degradation or introduce additional computation overhead. We propose a novel Focused Linear Attention module to achieve both high efficiency and expressiveness.
  arXiv  Detail & Related papers  (2023-08-01T10:37:12Z)
• Exploiting Inductive Bias in Transformer for Point Cloud Classification and Segmentation [22.587913528540465]
  In this paper, we design a new Inductive Bias-aided Transformer (IBT) method to learn 3D inter-point relations. Local feature learning is performed through Relative Position, Attentive Feature Pooling. We demonstrate its superiority experimentally on classification and segmentation tasks.
  arXiv  Detail & Related papers  (2023-04-27T12:17:35Z)
• Slide-Transformer: Hierarchical Vision Transformer with Local Self-Attention [34.26177289099421]
  Self-attention mechanism has been a key factor in the recent progress of Vision Transformer (ViT) We propose a novel local attention module, which leverages common convolution operations to achieve high efficiency, flexibility and generalizability. Our module realizes the local attention paradigm in both efficient and flexible manner.
  arXiv  Detail & Related papers  (2023-04-09T13:37:59Z)
• Adaptive Spot-Guided Transformer for Consistent Local Feature Matching [64.30749838423922]
  We propose Adaptive Spot-Guided Transformer (ASTR) for local feature matching. ASTR models the local consistency and scale variations in a unified coarse-to-fine architecture.
  arXiv  Detail & Related papers  (2023-03-29T12:28:01Z)
• Improving Transformer-based Image Matching by Cascaded Capturing Spatially Informative Keypoints [44.90917854990362]
  We propose a transformer-based cascade matching model -- Cascade feature Matching TRansformer (CasMTR) We use a simple yet effective Non-Maximum Suppression (NMS) post-process to filter keypoints through the confidence map. CasMTR achieves state-of-the-art performance in indoor and outdoor pose estimation as well as visual localization.
  arXiv  Detail & Related papers  (2023-03-06T04:32:34Z)
• CSformer: Bridging Convolution and Transformer for Compressive Sensing [65.22377493627687]
  This paper proposes a hybrid framework that integrates the advantages of leveraging detailed spatial information from CNN and the global context provided by transformer for enhanced representation learning. The proposed approach is an end-to-end compressive image sensing method, composed of adaptive sampling and recovery. The experimental results demonstrate the effectiveness of the dedicated transformer-based architecture for compressive sensing.
  arXiv  Detail & Related papers  (2021-12-31T04:37:11Z)
• LCTR: On Awakening the Local Continuity of Transformer for Weakly Supervised Object Localization [38.376238216214524]
  Weakly supervised object localization (WSOL) aims to learn object localizer solely by using image-level labels. We propose a novel framework built upon the transformer, termed LCTR, which targets at enhancing the local perception capability of global features.
  arXiv  Detail & Related papers  (2021-12-10T01:48:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.