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ICAA-Mamba: Vision Mamba for image Color Aesthetics Assessment

ICAA-Mamba: Vision Mamba for Image Color Aesthetics Assessme...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Qinghao Xie Wenchao Jiang Zhihua Wang Qiuping Jiang School of Computer Science Guangdong University of Technology Guangzhou China Department of Engineering Shenzhen MSU-BIT University Shenzhen China School of Information Science and Engineering Ningbo University Ningbo China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

image Color Aesthetics Assessment (ICAA) focuses on evaluating the aesthetic quality of color composition within images. This task involves analyzing and quantifying the visual appeal of color arrangements, taking into account factors such as harmony, contrast, and balance, to provide an objective assessment of color aesthetics. In this paper, we investigate the application of the State Space Model (Mamba) to the ICAA task, with a focus on exploring the perceptual capabilities of vision Mamba. To this end, we propose a novel framework that employs Mamba as the backbone to extract informative patterns from images, leveraging its global receptive field and linear complexity with respect to input length. Instead of relying solely on a final-layer feature, followed by fully connected layers for quality prediction, we exploit a comprehensive set of multi-scale features, thereby constructing a richer global representation of color information. To fuse these multi-scale features effectively, we introduce a Weighted Feature Fusion Module (WFFM), which adaptively assigns weights to emphasize salient color-related information. We conduct extensive experiments on two benchmark datasets, ICAA17K and SPAQ, successfully demonstrating its effectiveness for the ICAA task. The code is available at https://***/qinghaoya/icaa-mamba.

关键词： Deep learning visualization Codes image color analysis Fuses Signal processing Feature extraction Complexity theory Data mining Speech processing

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Collaborative Dual-Branch Spatial-Frequency Enhancement Network for Low-Light images

Collaborative Dual-Branch Spatial-Frequency Enhancement Netw...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Tao He Tiecheng Song Yin Liu Feng Yang Ruiyuan Chen Zhixin Li School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Low-light images are commonly present due to imaging factors such as insufficient light, night shooting and back lit. Existing low-light image enhancement (LLIE) methods typically rely on a low-light input image for enhancement, which seldom leverage information contained in its high-light counterpart to restore image structures and handle complex lighting, leading to unsatisfactory image quality. In view of this, in this paper we propose a Collaborative Dual-Branch Spatial-Frequency Enhancement Network (CDSE-Net). Specifically, we apply the inversion operation to low-light images to self-generate high-light images and build a collaborative dual-branch network which enhances images sequentially in spatial and frequency domains. In the spatial domain, we leverage adaptive curve estimation and multi-direction convolutions to restore lightness and structure information, respectively. In the frequency domain, we perform amplitude interactions on dual-branch images and 1×1 convolution on phase features to adjust image lightness and structures, respectively. Finally, we introduce an illumination-aware attention module to fuse two branches. Experiments on several widely used datasets quantitatively and qualitatively demonstrate the advantages of our network over state-of-the-art methods for LLIE.

关键词： image quality Convolution Fuses Frequency-domain analysis Collaboration Lighting Imaging Speech enhancement image restoration image reconstruction

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Towards Accurate Text-based image Captioning with Content Diversity Exploration

Towards Accurate Text-based Image Captioning with Content Di...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xu, Guanghui Niu, Shuaicheng Tan, Mingkui Luo, Yucheng Du, Qing Wu, Qi South China Univ Technol Guangzhou Peoples R China Pazhou Lab Guangzhou Peoples R China Univ Adelaide Adelaide SA Australia Minist Educ Key Lab Big Data & Intelligent Robot Hong Kong Peoples R China

ISBN: (纸本)9781665445092

Text-based image captioning (TextCap) which aims to read and reason images with texts is crucial for a machine to understand a detailed and complex scene environment, considering that texts are omnipresent in daily life. This task, however, is very challenging because an image often contains complex texts and visual information that is hard to be described comprehensively. Existing methods attempt to extend the traditional image captioning methods to solve this task, which focus on describing the overall scene of images by one global caption. This is infeasible because the complex text and visual information cannot be described well within one caption. To resolve this difficulty, we seek to generate multiple captions that accurately describe different parts of an image in detail. To achieve this purpose, there are three key challenges: 1) it is hard to decide which parts of the texts of images to copy or paraphrase;2) it is non-trivial to capture the complex relationship between diverse texts in an image;3) how to generate multiple captions with diverse content is still an open problem. To conquer these, we propose a novel Anchor-Captioner method. Specifically, we first find the important tokens which are supposed to be paid more attention to and consider them as anchors. Then, for each chosen anchor, we group its relevant texts to construct the corresponding anchor-centred graph (ACG). Last, based on different ACGs, we conduct the multi-view caption generation to improve the content diversity of generated captions. Experimental results show that our method not only achieves SOTA performance but also generates diverse captions to describe images.

关键词： visualization Computer vision image resolution Benchmark testing Pattern recognition Proposals Optical character recognition software

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SLAM Loop Detection Algorithm Based on Improved Bag-of-Words Model 5

SLAM Loop Detection Algorithm Based on Improved Bag-of-Words...

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5th International conference on Pattern Recognition and Artificial Intelligence, PRAI 2022

作者： Xi, Kun He, Jianjun Hao, Shuang Luo, Li Chengdu China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

Aiming at the problem that the vocabulary tree in the traditional bag-of-words model algorithm is not strong enough to represent the scene, which will affect the performance of the loop detection module in the visual SLAM system and produce accumulated errors, this paper proposes a slam loop detection algorithm based on the optimized bag-of-words model, improves the clustering algorithm for generating offline vocabulary tree, and uses the improved K-Means algorithm to build the vocabulary tree;At the same time, the image feature data of the experimental scene is used to update the vocabulary tree to improve the representation ability of the vocabulary tree. In this paper, the data sets of CityCenter, NewCollege, KITTI00 and KITTI06 are compared and analyzed experimentally. Compared with the experiments before and after updating the vocabulary tree, when the accuracy is 100%, the maximum recall rate of the algorithm on the four data sets is improved by 11.28%, 24.73%, 16.58% and 17.73% respectively;Then, compared with the five classical algorithms, the maximum recall rates of the four data sets are 0.39%, 11.27%, 2.39% and 22.67% higher than the best results of the five algorithms, respectively. The improved algorithm reduces the processing time, and the experimental results verify the correctness and effectiveness of the improved algorithm. © 2022 ieee.

关键词： image enhancement

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CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Downstream Vision Tasks Under Unknown Degradations

CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer fo...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yuwei Zhang Yan Wu Yanming Liu Xinyue Peng College of Electronic and Information Engineering Tongji University Shanghai China School of Software Technology Zhejiang University Hangzhou China College of Electronic and Information Engineering Southeast University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Extracting valuable visual cues for downstream vision tasks poses a particular challenge under unknown degradations. A straightforward solution is to preprocess images using image restoration methods, but their high computational complexity renders them unsuitable for real-time tasks. Recent efforts have aimed to enhance image representations optimized by downstream task losses. However, these approaches are confined to known single degradation scenarios, constraining their practical applicability in unpredictable environments. Therefore, we propose a Chain-of-thought Prompted Adaptive Enhancer, CPA-Enhancer, for enhancing important features crucial for downstream vision tasks under unknown degradations. Specifically, CPA-Enhancer progressively adapts its enhancement strategies under the step-by-step guidance of CoT prompts that encode degradation-related information. Overall, CPA-Enhancer is a plug-and-play lightweight enhancement model that can be integrated into any vision pipeline and trained with task-related losses, without any prior knowledge of the degradation type. Extensive experiments demonstrate that CPA-Enhancer significantly improves performance across various vision tasks under unknown degradations. The codes is available at https://***/zyw-stu/CPA-Enhancer.

关键词： Degradation Adaptation models visualization Pipelines Speech enhancement Signal processing image representation Real-time systems image restoration Computational complexity

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VRHI: Visibility Restoration for Hazy images Using a Haze Density Model

VRHI: Visibility Restoration for Hazy Images Using a Haze De...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Ju, Mingye Chen, Chuheng Liu, Juping Cheng, Kai Zhang, Dengyin Nanjing Univ Posts & Telecommun Nanjing Peoples R China Macquarie Univ Sydney NSW Australia

ISBN: (纸本)9781665448994

In this paper, a image processing method called VRHI is developed to enhance single hazy images. More specifically, inspired by visual characteristics of haze, a haze density estimation model is designed to predict the haze distribution. According to this recognized haze distribution, a quadtree based recursive strategy is subsequently proposed to locate the atmospheric light. Finally, by combining a global-wise adjusting mechanism and atmospheric scattering model, the haze cover in an image can be easily excluded using the estimated parameters. It is worth mentioning that VRHI is based on whole image to search the unknown parameters, thereby avoiding some unfavorable phenomena, e.g., over-enhancement and color distortion. Extensive experiments on real-world images and well-known dehazing datasets show that VRHI outperforms state-of-the-art techniques in robustness and effectiveness.

关键词： Training image color analysis Atmospheric modeling Refining Scattering Predictive models Distortion

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Knowledge Is Powerful: Art Knowledge-Driven Framework for Painting Style Classification Integrating Multimodal Knowledge

Knowledge Is Powerful: Art Knowledge-Driven Framework for Pa...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Haoyang Chen Chongjun Wang Yi Xin Lei Zhang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Paintings possess profound cultural and historical backgrounds. Unlike real-life images, they convey complex semantics beyond simple visual features. This diversity and complexity make painting style classification highly challenging, and many popular visual models struggle with it. To address this issue, we propose an art knowledge-driven framework(AKDF) to improve models’ comprehension of art knowledge. AKDF utilizes multimodal models and prompts to extract style-related textual descriptions from images. Text and image features will be fused in enhanced bilinear pooling module, thus integrating art knowledge into the output features. Additionally, we design a contrastive learning auxiliary task based on label embeddings to introduce further art knowledge about style labels. Besides, AKDF incorporates texture features and adds another genre classification auxiliary task to provide more painting information. We constructs two datasets based on WikiArt due to its comprehensive and challenging nature. The extensive experiment results demonstrate the superiority of the AKDF, which effectively develops the performance of various models by over three percentage points across two datasets.

关键词： visualization Art Semantics Contrastive learning Signal processing Feature extraction Complexity theory Cultural differences Speech processing Painting

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Multi-focus Fusion Using image Matting and Geometric Mean of DCT-Variance 1

引用

5th International conference on Computer Vision and image processing, CVIP 2020

作者： Roy, Manali Mukhopadhyay, Susanta Dhanbad India

ISBN: (数字)9789811610868

ISBN: (纸本)9789811610851

Optical lenses installed in most imaging devices suffer from the limited depth of field due to which objects get imaged with varying sharpness and details, thereby losing essential information. To cope with the problem, an effective multi-focus fusion technique is proposed in this paper based on a focus measure obtained from the statistical properties of an image and its DCT-bandpass filtered versions. The focus information obtained is eventually converted into a decision trimap using fundamental image processing operations. This trimap largely discriminates between the focussed and defocussed pixels and expedites the fusion process by acting as an input to a robust image matting framework to obtain a final decision map. Experimental results illustrate the superiority of the proposed approach over other competing multi-focus methods in terms of visual quality and fusion metrics. © 2021, Springer Nature Singapore Pte Ltd.

关键词： image fusion

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Generating Automatic Surgical Captions Using a Contrastive Language-image Pre-Training Model for Nephrectomy Surgery images

Generating Automatic Surgical Captions Using a Contrastive L...

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ieee Signal processing and communications Applications (SIU)

作者： Sevdenur Kütük Tuba Çağlıkantar Duygu Sarıkaya Bilgisayar Mühendisliği Ana Bilim Dalı Fen Bilimleri Enstitüsü Gazi Üniversitesi Ankara Türkiye Bilgisayar Mühendisliği Mühendislik Fakültesi Gazi Üniversitesi Ankara Türkiye School Of Computing University of Leeds Leeds United Kingdom

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Surgical reporting plays an important role in providing surgical feedback and medical training, however, it remains a complex task that requires clinical expertise. It also plays an important role in postoperative care and diagnosis of complications after surgery. Surgeons and surgical teams can use automatically generated surgical captions for effective and efficient surgical reporting. Automated surgical report generation has the potential to decrease surgeons’ workload and, therefore improve surgical outcomes. We utilized a customized Contrastive Language-image Pre-Training (CLIP) model in this research, employing VGG-19 as the image encoder and ClinicalBERT as the text encoder to produce automated surgical descriptions for nephrectomy surgery images. The model we named SurgicalClip achieved the following average scores: 0.702 BLEU-1, 0.51 BLEU-4, 3.615 CIDEr, 0.382 METEOR, and 0.657 ROUGE, and performed comparably to the benchmark models although it proposes a lightweight solution.

关键词： Training Surgery Signal processing Benchmark testing Meteors Task analysis Medical diagnostic imaging

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Semantics-Aware Gamma Correction for Unsupervised Low-Light image Enhancement

Semantics-Aware Gamma Correction for Unsupervised Low-Light ...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yu-Hsuan Chen Fu-Cheng Pan Yu-Chien Liao Jao-Hong Kao Yu-Chiang Frank Wang National Taiwan University Taiwan Novatek Inc. Taiwan

Low-light image enhancement aims to improve the visual quality of images captured under poor lighting conditions. While recent works have successfully developed deep learning-based solutions, a large number of existing works require ground-truth normal-light images during training, and most methods are not designed to exploit and preserve semantic information in the low-light inputs. In this paper, we propose a semantics-aware yet unsupervised low-light enhancement model based on gamma correction. Without observing ground-truth images or semantic annotations of the low-light inputs, our model learns via the introduced semantics-aware adversarial learning scheme with the associated objectives given a set of unpaired reference images of interest. Guided by such high-quality reference images and the inherent semantic practicality, our proposed method performs favorably against recent unsupervised low-light enhancement approaches.

关键词： Training visualization image segmentation Semantics Lighting Speech enhancement Signal processing

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