SAT: Selective Aggregation Transformer for Image Super-Resolution

• URL: http://arxiv.org/abs/2604.07994v2
• Date: Fri, 10 Apr 2026 03:37:09 GMT
• Title: SAT: Selective Aggregation Transformer for Image Super-Resolution
• Abstract summary: Transformer-based approaches have revolutionized image super-resolution by modeling long-range dependencies.<n>Recent window-based attention methods mitigate this by localizing computations, but they often yield restricted receptive fields.<n>This novel transformer efficiently captures long-range dependencies, leading to an enlarged model receptive field.<n>We show that SAT outperforms the state-of-the-art method PFT by up to 0.22dB, while the total number of FLOPs can be reduced by up to 27%.
• Score: 5.516233117319822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based approaches have revolutionized image super-resolution by modeling long-range dependencies. However, the quadratic computational complexity of vanilla self-attention mechanisms poses significant challenges, often leading to compromises between efficiency and global context exploitation. Recent window-based attention methods mitigate this by localizing computations, but they often yield restricted receptive fields. To mitigate these limitations, we propose Selective Aggregation Transformer (SAT). This novel transformer efficiently captures long-range dependencies, leading to an enlarged model receptive field by selectively aggregating key-value matrices (reducing the number of tokens by 97\%) via our Density-driven Token Aggregation algorithm while maintaining the full resolution of the query matrix. This design significantly reduces computational costs, resulting in lower complexity and enabling scalable global interactions without compromising reconstruction fidelity. SAT identifies and represents each cluster with a single aggregation token, utilizing density and isolation metrics to ensure that critical high-frequency details are preserved. Experimental results demonstrate that SAT outperforms the state-of-the-art method PFT by up to 0.22dB, while the total number of FLOPs can be reduced by up to 27\%.

Related papers

• CUST: Clustered Unit-level Similarity Transformer for Lightweight Image Super-Resolution [0.2538209532048866]
  Clustered Unit-level Similarity Transformer (CUST) is a novel architecture that efficiently integrates global and local information.<n>CUST enables each patch to aggregate and attend to similar patches within a broadened regional scope outside its local window.<n>Experiments demonstrate that our proposed model achieves a practical balance between computational efficiency and restoration performance.
  arXiv  Detail & Related papers  (2026-07-13T04:56:57Z)
• Computation of Least Trimmed Squares: A Branch-and-Bound framework with Hyperplane Arrangement Enhancements [21.28320211178358]
  The least trimmed squares (LTS) regression problem is a robust estimator that mitigates the influence of outliers in data by capping residuals with large magnitudes.<n>Existing mixed-integer optimization (MIO) formulations scale poorly due to weak relaxations and exponential worst-case complexity in the number of observations.<n>We propose a new MIO formulation that embeds hyperplane arrangement logic into a perspective reformulation, explicitly enforcing structural properties of optimal solutions.
  arXiv  Detail & Related papers  (2026-04-13T15:00:53Z)
• From Local Windows to Adaptive Candidates via Individualized Exploratory: Rethinking Attention for Image Super-Resolution [20.444907448992154]
  Single Image Super-Resolution (SISR) is a fundamental computer vision task that aims to reconstruct a high-resolution (HR) image from a low-resolution (LR) input.<n>We propose the Individualized Exploratory Transformer (IET) to enable flexible and token-adaptive attention computation.
  arXiv  Detail & Related papers  (2026-01-13T09:01:20Z)
• Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network [84.22115118596741]
  We propose an end-to-end deep learning (DL) framework with low inference complexity for symbol-level precoding.<n>We show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods.
  arXiv  Detail & Related papers  (2025-10-02T15:15:50Z)
• Task-Oriented Feature Compression for Multimodal Understanding via Device-Edge Co-Inference [54.53508601749513]
  We propose a task-oriented feature compression (TOFC) method for multimodal understanding in a device-edge co-inference framework.<n>To enhance compression efficiency, multiple entropy models are adaptively selected based on the characteristics of the visual features.<n>Results show that TOFC achieves up to 52% reduction in data transmission overhead and 63% reduction in system latency.
  arXiv  Detail & Related papers  (2025-03-17T08:37:22Z)
• MOLA: Enhancing Industrial Process Monitoring Using Multi-Block Orthogonal Long Short-Term Memory Autoencoder [3.7028696448588487]
  We introduce MOLA: a Multi-block Orthogonal Long short-term memory Autoencoder paradigm, to conduct accurate, reliable fault detection of industrial processes.<n>We propose a multi-block monitoring structure, which categorizes the process variables into multiple blocks by leveraging expert process knowledge.<n>We demonstrate the efficiency and effectiveness of our MOLA framework by applying it to the Tennessee Eastman Process.
  arXiv  Detail & Related papers  (2024-10-10T00:49:43Z)
• Transforming Image Super-Resolution: A ConvFormer-based Efficient Approach [58.57026686186709]
  We introduce the Convolutional Transformer layer (ConvFormer) and propose a ConvFormer-based Super-Resolution network (CFSR) CFSR inherits the advantages of both convolution-based and transformer-based approaches. Experiments demonstrate that CFSR strikes an optimal balance between computational cost and performance.
  arXiv  Detail & Related papers  (2024-01-11T03:08:00Z)
• Transformer-based Context Condensation for Boosting Feature Pyramids in Object Detection [77.50110439560152]
  Current object detectors typically have a feature pyramid (FP) module for multi-level feature fusion (MFF) We propose a novel and efficient context modeling mechanism that can help existing FPs deliver better MFF results. In particular, we introduce a novel insight that comprehensive contexts can be decomposed and condensed into two types of representations for higher efficiency.
  arXiv  Detail & Related papers  (2022-07-14T01:45:03Z)
• Adaptive Multi-Resolution Attention with Linear Complexity [18.64163036371161]
  We propose a novel structure named Adaptive Multi-Resolution Attention (AdaMRA) for short. We leverage a multi-resolution multi-head attention mechanism, enabling attention heads to capture long-range contextual information in a coarse-to-fine fashion. To facilitate AdaMRA utilization by the scientific community, the code implementation will be made publicly available.
  arXiv  Detail & Related papers  (2021-08-10T23:17:16Z)
• Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature Selection [138.97647716793333]
  We propose a simple and efficient unsupervised feature selection method, by combining reconstruction error with $l_2,p$-norm regularization. We present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically.
  arXiv  Detail & Related papers  (2020-12-29T04:08:38Z)
• Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation [72.40827239394565]
  We propose to compute features only at sparsely sampled locations. We then densely reconstruct the feature map with an efficient procedure. The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks.
  arXiv  Detail & Related papers  (2020-03-19T15:36:31Z)

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.