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Spk2ImgMamba: Spiking Camera image Reconstruction with Multi-Scale State Space Models

Spk2ImgMamba: Spiking Camera Image Reconstruction with Multi...

引用

2025 ieee/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Yin, Jiaoyang Fan, Bin Xu, Chao Huang, Tiejun Shi, Boxin School of Computer Science Peking University State Key Lab of Multimedia Info. Processing China School of Computer Science Peking University Nat'l Eng. Research Ctr. of Visual Technology China School of Intelligence Science and Technology Peking University Nat'l Key Lab of General Ai China

ISBN: (纸本)9798331510831

As a bio-inspired vision sensor, the spiking camera has showcased remarkable capability in high-speed imaging with a sampling rate of 40,000 Hz. Reconstructing clear images from continuous spike streams, which is obtained by each photosensor continuously detecting photons and firing them asynchronously, has garnered significant attention. Despite promising results, existing spike-to-image reconstruction methods face challenges in balancing global receptive fields and efficient computation due to the inherent limitations of their backbones. Recently, due to powerful long-range modeling and linear complexity, the state space model (SSM) has emerged as a competitive alternative to CNNs and Transformers. In this paper, we propose a lightweight spike-to-image reconstruction network that harnesses Mamba as the backbone. Our approach sequentially executes three core modules: temporal information integration, spatial feature enhancement, and progressive image reconstruction. The former accumulates cues across diverse temporal windows to explore both long-term and short-term contexts. Subsequently, to model global dependencies while heightening local detail perception, we develop a multi-scale SSM block characterized by multi-scale multi-direction scanning, which effectively boosts spatial feature representations. Finally, intensity images are decoded progressively from the enhanced light-intensity features. Extensive experiments on both synthetic and real-captured data demonstrate that our approach achieves state-of-the-art performance, with only 10% of the network parameters and nearly two orders of magnitude less computational effort. The code will be available at https://***/interstellarH/Spk2ImgMamba. © 2025 ieee.

关键词： Bioimaging

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3rd International conference on Computing and Communication Networks, ICCCN 2023

3rd International Conference on Computing and Communication ...

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3rd International conference on Computing and Communication Networks, ICCCN 2023

ISBN: (纸本)9789819726707

The proceedings contain 60 papers. The special focus in this conference is on Computing and Communication Networks. The topics include: Enhancing Security in Wireless Sensor Networks: A Broadcast/Multicast Authentication Framework with Identity-Based Signature Schemes;TSFTO: A Two-Stage Fuzzy-Based Tasks Orchestration Algorithm for Edge and Fog Computing Environments;tracking Climatic Variations Through Smart IoT-Driven Approach: An Exploratory Analysis;Design and Manufacturing of an Efficient Low-Cost Fire Surveillance System Based on GSM communications and IoT Technology;a Comparative Analysis of Optimized Routing Protocols for High-Performance Mobile Ad Hoc Networks;integrating Sandpiper Optimization Algorithm and Secure Aware Routing Protocol for Efficient Cluster Head Selection in Wireless Sensor Networks;the Effects of Weather Conditions on Data Transmission in Free Space for Wireless Sensor Networks;Using Wireless Sensor Network Performance and Optimizing in Underground Mines with Virtual MIMO Antenna;a Novel Logistic Regression-Based Fire Detection Model Using IoT in Underground Coal Mines;An MQTT IoT Intrusion Detection System Using Deep-Learning;multi-objective Optimal Feature Selection for Cyber Security Integrated with Deep Learning;application of Gradient Boosting Classifier-Based Computational Intelligence to Detect Drug Addiction Threat in Society;insurance 4.0: Smart Insurance Technologies and Challenges Ahead;review on Vision Transformer for Satellite image Classification;primitive Roots and Euler Totient Function-Based Progressive visual Cryptography;approach for Fire Detection Using image processing;review of Various Approaches for Authorship Identification in Digital Forensics;machine Learning Model for Traffic Prediction and Pattern Extraction in High-Speed Optical Networks;improved Markov Decision Process in Wireless Sensor Network for Optimal Energy Consumption.

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VPCI: Self-Supervised visual Prompt-Guided Cross-Domain Interactive image Fusion Framework

VPCI: Self-Supervised Visual Prompt-Guided Cross-Domain Inte...

引用

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

作者： Yong Liu Chengyu Wu Jiayuan Cui Bin Jiang Shenzhen Research Institute of Shandong University Shenzhen China School of Mechanical Electrical & Information Engineering Shandong University Weihai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

image fusion combines information from multi-modality images to produce high-quality fused images with enhanced clarity, contrast, and informativeness. However, limited ground truth fusion data lead to difficulties in effectively training these fusion models. Moreover, current studies lack of fine-grained, domain style-specific guidance during image fusion processing. To address these issues, we propose a self-supervised visual Prompt-guided Cross-Domain Interactive image fusion framework (VPCI), which introduces a visual Prompt Generation (VPG) strategy that embeds visual features from different modalities as prompts, guiding the model to learn complementary information. Furthermore, to better integrate modality-specific styles, we introduce a Cross-domain Prompt-guided Interaction (CPI) module to generate information-rich fusion images by fully interacting with visual prompts. Experimental results show that our framework outperforms existing state-of-the-art methods. The code is available at https://***/liwead/VPCI.

关键词： Training visualization Codes Signal processing Data models Acoustics High frequency Speech processing image fusion

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Language-Guided Semantic Alignment for Point Cloud-Based 3D Scene Graph Generation

Language-Guided Semantic Alignment for Point Cloud-Based 3D ...

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2025 International conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Suhyeon Jeong, Jinwoo Jeong, Taehyun Kim, Sungjei Intelligent Image Processing Research Center Korea Electronics Technology Institute Seongnam Korea Republic of School of Computer Science and Engineering Korea University of Technology and Education Cheonan Korea Republic of

ISBN: (纸本)9798331510756

In this paper, we propose a novel model called LAP, Language-guided semantic Alignment for Point cloud-based 3D semantic scene graph generation. Point cloud-based 3D scene graph generation is the task of predicting the category of each instance present in a 3D point cloud scene and the relationships between them. Our model leverages text embeddings for objects, predicates, and triplets during network training, enabling the network to generate reliable semantic scene graphs. To this end, we introduce three loss functions that leverage text embed dings to semantically align the features for scene graph prediction. Comparative experiments with previous methods on the 3DSSG dataset demonstrate the effectiveness of the LAP model. © 2025 ieee.

关键词： Cloud platforms

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Triplet Synthesis for Enhancing Composed image Retrieval via Counterfactual image Generation

Triplet Synthesis for Enhancing Composed Image Retrieval via...

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

作者： Kenta Uesugi Naoki Saito Keisuke Maeda Takahiro Ogawa Miki Haseyama Hokkaido University Japan

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Composed image Retrieval (CIR) provides an effective way to manage and access large-scale visual data. Construction of the CIR model utilizes triplets that consist of a reference image, modification text describing desired changes, and a target image that reflects these changes. For effectively training CIR models, extensive manual annotation to construct high-quality training datasets, which can be time-consuming and labor-intensive, is required. To deal with this problem, this paper proposes a novel triplet synthesis method by leveraging counterfactual image generation. By controlling visual feature modifications via counterfactual image generation, our approach automatically generates diverse training triplets without any manual intervention. This approach facilitates the creation of larger and more expressive datasets, leading to the improvement of CIR model’s performance.

关键词： Training visualization image synthesis Annotations image retrieval Manuals Signal processing Acoustics Speech processing

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Analysis the Bacterial Effect of Leaf Using Colour image processing

Analysis the Bacterial Effect of Leaf Using Colour Image Pro...

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2025 International conference on visual Analytics and Data visualization, ICVADV 2025

作者： Nivethitha, T. Sanjay, P. Sudhir Siddharth, E. Shruthika, S. Sathana, S. Sri Eshwar College of Engineering Department of Ece Coimbatore India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

The bacterial effect of leaf disease are being employed to address the significant crop losses caused by various rice leaf diseases. The proposed solution provides real-time disease diagnosis, giving farmers a cost-effective and efficient tool for monitoring crop health. It will reduce the use of harmful pesticides by detecting disease earlier and improve rice yield and quality. Recent studies highlight that DCNNs are among the most effective tools for detecting and categorizing these diseases. By training the model on datasets containing leaf images, it extracts critical features to distinguish between healthy and infected leaves. This architecture ensures high detection accuracy under diverse environmental conditions and across multiple disease types. The proposed approach offers real-time diagnostics, giving farmers an affordable and practical method to monitor crop health efficiently. Early detection also helps reduce pesticide usage, resulting in better crop quality and increased yields. Additionally, the model's adaptability is enhanced with a feedback loop that integrates new data, allowing continuous improvement and expanding its ability to detect different diseases. With its high accuracy, this DCNN-based system provides a reliable solution for precision agriculture and promotes sustainable farming practices. © 2025 ieee.

关键词： Photointerpretation

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A Novel visual Communication Scenario Based on image processing Algorithms 2

A Novel Visual Communication Scenario Based on Image Process...

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2nd International conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Yang, Jinhua Hou, Li Xijing University Shaanxi Xi'an710123 China

ISBN: (纸本)9798331505745

In order to comply with the trend of intelligent visual communication, this study proposed an innovative visual communication scenario based on image processing algorithms. The framework aims to optimize traditional key technologies such as the image generation, editing, style transfer and image compression. First, as the foundation of visual communication, this study proposes a generative adversarial network model based on text semantic information for image generation and editing. The model achieves stable image generation and efficient editing from a theoretical level through paired training of text and image pairs. Secondly, for image style transfer, this study designed an improved VGG19 convolutional neural network. At the same time, the adaptive instance normalization technology was combined to optimize the effect of style transfer. Finally, in terms of image compression, the study proposed an improved generative adversarial network (REVISED-GAN) model. This model can dynamically adjust the compression error based on structured information to improve image compression efficiency. Through comparative tests, the proposed image style transfer and image compression algorithms have shown excellent performance in terms of structural similarity, image quality and compression ratio. © 2025 ieee.

关键词： visual communication

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Artistic image Aesthetics Assessment Assisted by Photographic visual Attributes

Artistic Image Aesthetics Assessment Assisted by Photographi...

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

作者： Haiyong Tang Yihua Chen Xiaoping Liang Lv Chen Pengsheng Huang Zhenjun Tang Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin China Beijing MeiyiLab Co. Ltd. Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Most data-driven deep learning-based Artistic image Aesthetics Assessment (AIAA) methods cannot effectively extract visual attributes from art images since the existing artistic image datasets don’t provide any information about visual attributes. The lack of visual attributes reduces the interpretability of AIAA methods and limits their performance. To address these problems, a novel artistic image aesthetics assessment assisted by photographic visual attributes is proposed. The proposed method consists of a feature extraction module and a joint prediction module. The feature extraction module pre-trained on a photographic dataset and an artistic image dataset can learn the information of photographic attributes and the generic artistic aesthetic information. The joint prediction module uses a non-local self-attention block to fuse the photographic visual attribute features with general artistic aesthetic features. The fused features are fed into an FC layer for calculating the artistic image aesthetic score. Experimental results indicate that our proposed method outperforms some state-of-the-art AIAA methods.

关键词： visualization Art Fuses Signal processing Benchmark testing Feature extraction Transformers Acoustics Data mining Speech processing

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Two-stream Semantic Alignment Networks for Multi-label image Classification

Two-stream Semantic Alignment Networks for Multi-label Image...

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

作者： Wenlan Kuang Zhixin Li Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent research ideas mainly focus on solving the semantic consistency of visual features and label features. However, since images contain complex scene content, the features captured by visual feature extraction networks based on grid or sequence representation may introduce redundant information or lack continuity when identifying irregular objects. In order to fully mine the visual information of complex objects in images and enhance the inter-modal interaction of images and labels, we introduce a flexible graph structure to explore the internal information of objects and design a Two-stream Semantic Alignment Networks (TsSAN) for multi-label image classification. To enhance the context awareness and semantic association of different patch regions, we propose a semantic-augmented interaction module that combines two kinds of visual semantic information with label embeddings for interactive learning. Finally, we refine the dependence between local intrinsic information and overall semantics by redefining semantic queries through semantically enhanced visual spatial features and graph aggregation features. Experiments on three public datasets demonstrate the effectiveness of our TsSAN.

关键词： Representation learning visualization image coding Semantics Signal processing image representation Feature extraction Object recognition Speech processing image classification

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ViSeNet: A visual-Semantic Fusion Network for Enhancing Few-Shot Classification

ViSeNet: A Visual-Semantic Fusion Network for Enhancing Few-...

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ieee International conference on Consumer Electronics (ICCE)

作者： Dongheon Lee Sangwoo Yun Joonki Paik Image Processing and Intelligent & System Laboratory Graduate School of Artificial Intelligence Chung-Ang University Seoul Korea Image Processing and Intelligent & System Laboratory Graduate School of Advanced & Imaging Science Multimedia and Film Chung-Ang University Seoul Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Few-shot learning addresses the challenge of recognizing novel classes with only a few labeled examples, a critical issue in limited data scenarios. This paper proposes a multi-modal visual and Semantic Network (ViSeNet), which enhances few-shot classification by mining linguistic features and fusing weighted vectors. ViSeNet generates enriched class prototypes by leveraging both visual and semantic information. The linguistic feature mining process utilizes image-to-text models to produce class-specific textual descriptions, which are then transformed into representative semantic embeddings. For visual feature extraction, we employ a pretrained Swin Transformer, while text embeddings are integrated for each class using weighted factors. By combining these modalities, ViSeNet constructs class prototypes that capture both visual and semantic characteristics. Experimental results on benchmark datasets, particularly CIFAR-FS, demonstrate that ViSeNet achieves performance comparable to state-of-the-art approaches.

关键词： visualization Semantics Prototypes Linguistics Transformers Feature extraction Vectors Data mining Few shot learning Consumer electronics

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