Efficient Learnable Collaborative Attention for Single Image Super-Resolution

• URL: http://arxiv.org/abs/2404.04922v1
• Date: Sun, 7 Apr 2024 11:25:04 GMT
• Title: Efficient Learnable Collaborative Attention for Single Image Super-Resolution
• Authors: Yigang Zhao Chaowei Zheng, Jiannan Su, GuangyongChen, MinGan,
• Abstract summary: Non-Local Attention (NLA) is a powerful technique for capturing long-range feature correlations in deep single image super-resolution (SR) We propose a novel Learnable Collaborative Attention (LCoA) that introduces inductive bias into non-local modeling. Our LCoA can reduce the non-local modeling time by about 83% in the inference stage.
• Score: 18.955369476815136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Non-Local Attention (NLA) is a powerful technique for capturing long-range feature correlations in deep single image super-resolution (SR). However, NLA suffers from high computational complexity and memory consumption, as it requires aggregating all non-local feature information for each query response and recalculating the similarity weight distribution for different abstraction levels of features. To address these challenges, we propose a novel Learnable Collaborative Attention (LCoA) that introduces inductive bias into non-local modeling. Our LCoA consists of two components: Learnable Sparse Pattern (LSP) and Collaborative Attention (CoA). LSP uses the k-means clustering algorithm to dynamically adjust the sparse attention pattern of deep features, which reduces the number of non-local modeling rounds compared with existing sparse solutions. CoA leverages the sparse attention pattern and weights learned by LSP, and co-optimizes the similarity matrix across different abstraction levels, which avoids redundant similarity matrix calculations. The experimental results show that our LCoA can reduce the non-local modeling time by about 83% in the inference stage. In addition, we integrate our LCoA into a deep Learnable Collaborative Attention Network (LCoAN), which achieves competitive performance in terms of inference time, memory consumption, and reconstruction quality compared with other state-of-the-art SR methods.

Related papers

• Q-VLM: Post-training Quantization for Large Vision-Language Models [73.19871905102545]
  We propose a post-training quantization framework of large vision-language models (LVLMs) for efficient multi-modal inference. We mine the cross-layer dependency that significantly influences discretization errors of the entire vision-language model, and embed this dependency into optimal quantization strategy. Experimental results demonstrate that our method compresses the memory by 2.78x and increase generate speed by 1.44x about 13B LLaVA model without performance degradation.
  arXiv  Detail & Related papers  (2024-10-10T17:02:48Z)
• Optimization by Parallel Quasi-Quantum Annealing with Gradient-Based Sampling [0.0]
  This study proposes a different approach that integrates gradient-based update through continuous relaxation, combined with Quasi-Quantum Annealing (QQA) Numerical experiments demonstrate that our method is a competitive general-purpose solver, achieving performance comparable to iSCO and learning-based solvers.
  arXiv  Detail & Related papers  (2024-09-02T12:55:27Z)
• ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution [76.7408734079706]
  Single hyperspectral image super-resolution (single-HSI-SR) aims to restore a high-resolution hyperspectral image from a low-resolution observation. We propose ESSAformer, an ESSA attention-embedded Transformer network for single-HSI-SR with an iterative refining structure.
  arXiv  Detail & Related papers  (2023-07-26T07:45:14Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• Towards Lightweight Cross-domain Sequential Recommendation via External Attention-enhanced Graph Convolution Network [7.1102362215550725]
  Cross-domain Sequential Recommendation (CSR) depicts the evolution of behavior patterns for overlapped users by modeling their interactions from multiple domains. We introduce a lightweight external attention-enhanced GCN-based framework to solve the above challenges, namely LEA-GCN. To further alleviate the framework structure and aggregate the user-specific sequential pattern, we devise a novel dual-channel External Attention (EA) component.
  arXiv  Detail & Related papers  (2023-02-07T03:06:29Z)
• Unifying Synergies between Self-supervised Learning and Dynamic Computation [53.66628188936682]
  We present a novel perspective on the interplay between SSL and DC paradigms. We show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2023-01-22T17:12:58Z)
• Towards Stable Co-saliency Detection and Object Co-segmentation [12.979401244603661]
  We present a novel model for simultaneous stable co-saliency detection (CoSOD) and object co-segmentation (CoSEG) We first propose a multi-path stable recurrent unit (MSRU), containing dummy orders mechanisms (DOM) and recurrent unit (RU) Our proposed MSRU not only helps CoSOD (CoSEG) model captures robust inter-image relations, but also reduces order-sensitivity, resulting in a more stable inference and training process.
  arXiv  Detail & Related papers  (2022-09-25T03:58:49Z)
• Efficient Non-Local Contrastive Attention for Image Super-Resolution [48.093500219958834]
  Non-Local Attention (NLA) brings significant improvement for Single Image Super-Resolution (SISR) by leveraging intrinsic feature correlation in natural images. We propose a novel Efficient Non-Local Contrastive Attention (ENLCA) to perform long-range visual modeling and leverage more relevant non-local features.
  arXiv  Detail & Related papers  (2022-01-11T05:59:09Z)
• Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining [66.82470461139376]
  We propose the first Cross-Scale Non-Local (CS-NL) attention module with integration into a recurrent neural network. By combining the new CS-NL prior with local and in-scale non-local priors in a powerful recurrent fusion cell, we can find more cross-scale feature correlations within a single low-resolution image.
  arXiv  Detail & Related papers  (2020-06-02T07:08:58Z)

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.