CUST: Clustered Unit-level Similarity Transformer for Lightweight Image Super-Resolution

• URL: http://arxiv.org/abs/2607.11088v1
• Date: Mon, 13 Jul 2026 04:56:57 GMT
• Title: CUST: Clustered Unit-level Similarity Transformer for Lightweight Image Super-Resolution
• Abstract summary: 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.
• Score: 0.2538209532048866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, Vision Transformer (ViT)-based models have exhibited remarkable performance in image super-resolution. However, the quadratic computational complexity of ViTs with respect to spatial resolution severely constrains their efficiency, leading to high latency and massive memory consumption. To alleviate this, various window-based attention mechanisms have been proposed; yet, they inherently compromise the long-range dependency modeling that is the primary advantage of ViTs. To overcome these limitations, we propose the Clustered Unit-level Similarity Transformer (CUST), a novel architecture that efficiently integrates global and local information. Specifically, CUST enables each patch to aggregate and attend to similar patches within a broadened regional scope outside its local window, thereby capturing extensive contextual understanding. Furthermore, it employs overlapping attention windows to capture local dependencies, while explicitly extracting high-frequency details by computing the residual difference between the original features and their downsampled-upsampled counterparts. Comprehensive experiments demonstrate that our proposed model achieves a practical balance between computational efficiency and restoration performance. It achieves a lower memory footprint and faster inference speed compared to recent global context or lightweight models under realistic constraints. Code is available at [https://github.com/jwgdmkj/CUST].

Related papers

• When Simplicity Wins: Bottleneck-Aware Context Modeling for Lightweight Semantic Segmentation [58.325533783810016]
  We propose SiConMo, a lightweight yet effective framework for semantic segmentation.<n>We show that simplicity arises from a key design principle: at very low computational budgets, the bottleneck is the most efficient stage to integrate local and global context.<n> experiments on ADE20K, PASCAL Context, Cityscapes, and COCO-Stuff demonstrate that SiConMo achieves a state-of-the-art accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2026-08-19T14:47:07Z)
• RepSFNet : A Single Fusion Network with Structural Reparameterization for Crowd Counting [6.895355763564631]
  RepSFNet is a lightweight architecture designed for accurate and real-time crowd estimation.<n>RepSFNet achieves competitive accuracy while reducing inference latency by up to 34 percent compared to recent state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-01-28T08:33:36Z)
• Dynamic Granularity Matters: Rethinking Vision Transformers Beyond Fixed Patch Splitting [15.751224470424786]
  Vision Transformers (ViTs) have demonstrated strong capabilities in capturing global dependencies but often struggle to efficiently represent fine-grained local details.<n>We propose Granularity-driven Vision Transformer (Grc-ViT), a dynamic coarse-to-fine framework that adaptively adjusts visual granularity based on image complexity.<n>Two learnable parameters, and beta, are optimized end-to-end to balance global reasoning and local perception.
  arXiv  Detail & Related papers  (2025-11-24T11:55:22Z)
• PiT: Progressive Diffusion Transformer [50.46345527963736]
  Diffusion Transformers (DiTs) achieve remarkable performance within image generation via the transformer architecture.<n>We find that DiTs do not rely as heavily on global information as previously believed.<n>We propose a series of Pseudo Progressive Diffusion Transformer (PiT)
  arXiv  Detail & Related papers  (2025-05-19T15:02:33Z)
• Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptation [158.37640586809187]
  Restoring any degraded image efficiently via just one model has become increasingly significant.<n>Our approach, termed AnyIR, takes a unified path that leverages inherent similarity across various degradations.<n>To fuse the degradation awareness and the contextualized attention, a spatial-frequency parallel fusion strategy is proposed.
  arXiv  Detail & Related papers  (2025-04-19T09:54:46Z)
• LIFT: Latent Implicit Functions for Task- and Data-Agnostic Encoding [4.759109475818876]
  Implicit Neural Representations (INRs) are proving to be a powerful paradigm in unifying task modeling across diverse data domains.<n>We introduce LIFT, a novel, high-performance framework that captures multiscale information through meta-learning.<n>We also introduce ReLIFT, an enhanced variant of LIFT that incorporates residual connections and expressive frequency encodings.
  arXiv  Detail & Related papers  (2025-03-19T17:00:58Z)
• Efficient High-Resolution Visual Representation Learning with State Space Model for Human Pose Estimation [60.80423207808076]
  Capturing long-range dependencies while preserving high-resolution visual representations is crucial for dense prediction tasks such as human pose estimation.<n>We propose the Dynamic Visual State Space (DVSS) block, which augments visual state space models with multi-scale convolutional operations.<n>We build HRVMamba, a novel model for efficient high-resolution representation learning.
  arXiv  Detail & Related papers  (2024-10-04T06:19:29Z)
• 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)
• Factorization Vision Transformer: Modeling Long Range Dependency with Local Window Cost [25.67071603343174]
  We propose a factorization self-attention mechanism (FaSA) that enjoys both the advantages of local window cost and long-range dependency modeling capability. FaViT achieves high performance and robustness, with linear computational complexity concerning input image spatial resolution. Our FaViT-B2 significantly improves classification accuracy by 1% and robustness by 7%, while reducing model parameters by 14%.
  arXiv  Detail & Related papers  (2023-12-14T02:38:12Z)
• Low-Resolution Self-Attention for Semantic Segmentation [93.30597515880079]
  We introduce the Low-Resolution Self-Attention (LRSA) mechanism to capture global context at a significantly reduced computational cost.<n>Our approach involves computing self-attention in a fixed low-resolution space regardless of the input image's resolution.<n>We demonstrate the effectiveness of our LRSA approach by building the LRFormer, a vision transformer with an encoder-decoder structure.
  arXiv  Detail & Related papers  (2023-10-08T06:10:09Z)
• 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)
• Learned Queries for Efficient Local Attention [11.123272845092611]
  Self-attention mechanism in vision transformers suffers from high latency and inefficient memory utilization. We propose a new shift-invariant local attention layer, called query and attend (QnA), that aggregates the input locally in an overlapping manner. We show improvements in speed and memory complexity while achieving comparable accuracy with state-of-the-art models.
  arXiv  Detail & Related papers  (2021-12-21T18:52:33Z)

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.