Enhancing Layer Interaction Using Key-Correlated Layer Attention

• URL: http://arxiv.org/abs/2606.28405v1
• Date: Wed, 24 Jun 2026 13:54:52 GMT
• Title: Enhancing Layer Interaction Using Key-Correlated Layer Attention
• Abstract summary: Key-Correlated Layer Attention (KCLA) is inspired by our observation that Key representations in layer attention exhibit high cosine similarity.<n>KCLA achieves linear computational complexity while preserving dynamic information updates.<n>It delivers good performance across diverse tasks, including image recognition, object detection, and medical image segmentation.
• Score: 12.760214212097795
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in network architecture design have introduced layer attention to enhance inter-layer interactions. In such frameworks, each layer queries all preceding layers to establish cross-layer connections. However, layer attention results in quadratic computational complexity with respect to network depth. To mitigate this issue, prior works have proposed Recurrent Layer Attention (RLA) and linear attention mechanisms, which suffer from static information updates and limited long-range cross-layer dependency modeling. To overcome these limitations, we propose Key-Correlated Layer Attention (KCLA), inspired by our observation that Key representations in layer attention exhibit high cosine similarity. KCLA achieves linear computational complexity while preserving dynamic information updates, directly derived from the foundational definition of layer attention. Furthermore, KCLA maintains long-range cross-layer connections and features a fixed spatial complexity, independent of network depth. Empirical evaluations demonstrate that KCLA delivers good performance across diverse tasks, including image recognition, object detection, and medical image segmentation. The code is publicly available at https://github.com/bgx666/KCLA.

Related papers

• KeepLoRA++: Continual Learning with Layer-Scaled Residual Gradient Adaptation [67.23448686598414]
  Continual learning for pre-trained vision-language models requires balancing three competing objectives.<n>This paper presents KeepLoRA++, balancing these objectives through a unified dual-dimensional knowledge retention mechanism.
  arXiv  Detail & Related papers  (2026-06-15T05:57:59Z)
• Dynamic Cross-Modal Feature Interaction Network for Hyperspectral and LiDAR Data Classification [66.59320112015556]
  Hyperspectral image (HSI) and LiDAR data joint classification is a challenging task.<n>We propose a novel Dynamic Cross-Modal Feature Interaction Network (DCMNet)<n>Our approach introduces three feature interaction blocks: Bilinear Spatial Attention Block (BSAB), Bilinear Channel Attention Block (BCAB), and Integration Convolutional Block (ICB)
  arXiv  Detail & Related papers  (2025-03-10T05:50:13Z)
• Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals [0.0]
  We propose a novel approach to quantify redundancy by leveraging the Kullback-Leibler (KL) divergence between adjacent layers.<n>We also introduce an Enhanced Beta Quantile Mapping (EBQM) method that accurately identifies and skips redundant layers.<n>Our proposed Efficient Layer Attention (ELA) architecture, improves both training efficiency and overall performance, achieving a 30% reduction in training time.
  arXiv  Detail & Related papers  (2025-03-09T06:20:11Z)
• Hierarchical Graph Interaction Transformer with Dynamic Token Clustering for Camouflaged Object Detection [57.883265488038134]
  We propose a hierarchical graph interaction network termed HGINet for camouflaged object detection. The network is capable of discovering imperceptible objects via effective graph interaction among the hierarchical tokenized features. Our experiments demonstrate the superior performance of HGINet compared to existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-27T12:53:25Z)
• Strengthening Layer Interaction via Dynamic Layer Attention [12.341997220052486]
  Existing layer attention methods achieve layer interaction on fixed feature maps in a static manner. To restore the dynamic context representation capability of the attention mechanism, we propose a Dynamic Layer Attention architecture. Experimental results demonstrate the effectiveness of the proposed DLA architecture, outperforming other state-of-the-art methods in image recognition and object detection tasks.
  arXiv  Detail & Related papers  (2024-06-19T09:35:14Z)
• Dense Network Expansion for Class Incremental Learning [61.00081795200547]
  State-of-the-art approaches use a dynamic architecture based on network expansion (NE), in which a task expert is added per task. A new NE method, dense network expansion (DNE), is proposed to achieve a better trade-off between accuracy and model complexity. It outperforms the previous SOTA methods by a margin of 4% in terms of accuracy, with similar or even smaller model scale.
  arXiv  Detail & Related papers  (2023-03-22T16:42:26Z)
• Kernel function impact on convolutional neural networks [10.98068123467568]
  We study the usage of kernel functions at the different layers in a convolutional neural network. We show how one can effectively leverage kernel functions, by introducing a more distortion aware pooling layers. We propose Kernelized Dense Layers (KDL), which replace fully-connected layers.
  arXiv  Detail & Related papers  (2023-02-20T19:57:01Z)
• Dynamic Scene Deblurring Base on Continuous Cross-Layer Attention Transmission [6.3482616879743885]
  We introduce a new continuous cross-layer attention transmission (CCLAT) mechanism that can exploit hierarchical attention information from all the convolutional layers. Taking RDAFB as the building block, we design an effective architecture for dynamic scene deblurring named RDAFNet. Experiments on benchmark datasets show that the proposed model outperforms the state-of-the-art deblurring approaches.
  arXiv  Detail & Related papers  (2022-06-23T04:55:13Z)
• Adaptive Cross-Layer Attention for Image Restoration [10.795382525098917]
  Non-local attention processes features of each layer separately, so it risks missing correlation between features among different layers. We propose a novel Adaptive Cross-Layer Attention (ACLA) module. Two adaptive designs are proposed for ACLA: (1) adaptively selecting the keys for non-local attention at each layer; (2) automatically searching for the insertion locations for ACLA modules.
  arXiv  Detail & Related papers  (2022-03-04T22:16:18Z)
• Cross-layer Feature Pyramid Network for Salient Object Detection [102.20031050972429]
  We propose a novel Cross-layer Feature Pyramid Network to improve the progressive fusion in salient object detection. The distributed features per layer own both semantics and salient details from all other layers simultaneously, and suffer reduced loss of important information.
  arXiv  Detail & Related papers  (2020-02-25T14:06:27Z)
• Convolutional Networks with Dense Connectivity [59.30634544498946]
  We introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks.
  arXiv  Detail & Related papers  (2020-01-08T06:54:53Z)

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.