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Seg-diffusion: Text-to-Image Diffusion Model for Open-Vocabulary Semantic Segmentation

Seg-diffusion: Text-to-Image Diffusion Model for Open-Vocabu...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shuo Zhang Jiaming Huang Yan Wu Tao Hu Wenbing Tang Jing Liu Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Huolala Shenzhen China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Open-vocabulary semantic segmentation (OVSS) is a challenging computer vision task that labels each pixel within an image based on text descriptions. Recent advancements in OVSS are largely attributed to the increased model capacity. However, these models often struggle with unfamiliar images or unseen text, as their visual language understanding is limited to training data. Text-to-image (T2I) diffusion models have demonstrated strong image generation with diverse open-vocabulary descriptions. It prompted us to explore whether the comprehensive priors in T2I diffusion models could enhance the zero-shot generalization of OVSS. In this study, we define OVSS as a denoising diffusion task from noisy to object mask and introduce Seg-diffusion, a novel method based on Stable Diffusion that utilizes its extensive visual and linguistic prior knowledge. Specifically, the object mask diffuses from ground-truth to a random distribution in latent space. The model learns to reverse this noisy process to reconstruct object mask to segment objectives using text embeddings with our proposed Content Attention Module (CAM). Extensive experiments on popular OVSS benchmarks show that Seg-diffusion outperforms previous well-established methods and achieves impressive zero-shot generalization to unseen datasets.

关键词： visualization Semantic segmentation Computational modeling Semantics Text to image Training data Signal processing Diffusion models Noise measurement Speech processing

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The State and Optimization of VR Technology in Real Estate: A Big data and visualization Approach 24

The State and Optimization of VR Technology in Real Estate: ...

引用

Proceedings of the 2024 5th international conference on Big data Economy and Information Management

作者： Xiliang Sun Mingze Wu International College Beijing China Agricultural University Beijing China

ISBN: (纸本)9798400711862

With the rapid development of global information technology, Virtual Reality (VR) has started to deeply affect the operations and market ecosystems of China's real estate sector. Research has shown that incorporating VR into the property sales and marketing, and presentation processes can enhance the operational performance and engagement of customers. Using both personal study and existing literature, this paper applies the SWOT analysis framework and incorporates big data methods to analyze China's VR real estate market from four aspects: strengths, weaknesses, opportunities, and threats. The findings show that VR can offer a very realistic virtual view and thus speed up the decision-making process, however, it is still faced with issues of technical failures and privacy concerns. In addition, the paper explores how VR can be integrated with artificial intelligence (AI), and applied to the commercial real estate and rental markets. Ahead, suppose real estate companies are able to harness big data platforms for user behavior analytics and targeted advertising. In that case, VR in China's property sector is likely to continue to develop strongly.

关键词： Digital transformation

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Leveraging visual Language Information for Enhanced Person Re-Identification: A Robust Framework for Real-World Applications

Leveraging Visual Language Information for Enhanced Person R...

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IEEE international conference on Consumer Electronics (ICCE)

作者： Seunghee Han Dasol Jeong Hasil Park Jiwon Park Hyebean Lee Joonki Paik Department of Image Chung-Ang University Seoul Korea Department of Artificial Intelligence Chung-Ang University Seoul Korea

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Recently, performance improvements through vision-language learning have gained significant attention in the field of image re-identification (ReID). In particular, the CLIP-ReID [1] achieved enhanced visual representations by introducing learnable text token sets for each identity. However, existing approaches have not fully exploited fine-grained part-specific features of the human body, which are crucial when applying person re-identification technology in real-world scenarios where partial occlusions or other factors might affect accuracy. To address this limitation, we propose a novel framework that incorporates body part-specific learnable text tokens, leveraging visual language information related to specific body regions. This approach is essential for mitigating the impact of occlusions and enhancing the robustness of ReID systems in practical settings. The process of applying visual language information through CLIP-ReID involves several stages. First, body part regions are identified and segmented within the image. Next, visual features specific to these regions are extracted and mapped to corresponding text tokens that describe these features in natural language. Finally, these text tokens are integrated with the image features to create a more comprehensive representation that captures both visual and semantic aspects, improving the discrimination of individuals based on specific anatomical features. Experimental results not only using the MSMT17 [2] and Market-1501 [3] datasets but also from a real-world scenario involving 11 CCTV cameras installed on the campus of Chung-Ang University demonstrated that the proposed method significantly outperformed existing models. This confirms the effectiveness of vision-language learning that utilizes segmented body part information in the image re-identification task.

关键词： Deformable models visualization Image segmentation Deformation Semantics Natural languages data visualization Feature extraction Robustness Identification of persons

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Event Masked Autoencoder: Point-wise Action Recognition with Event-Based Cameras

Event Masked Autoencoder: Point-wise Action Recognition with...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Jingkai Sun Qiang Zhang Jiaxu Wang Jiahang Cao Hao Cheng Renjing Xu Hong Kong University of Science and Technology Guangzhou

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dynamic vision sensors (DVS) are bio-inspired devices that capture visual information in the form of asynchronous events, which encode changes in pixel intensity with high temporal resolution and low latency. These events provide rich motion cues that can be exploited for various computer vision tasks, such as action recognition. However, most existing DVS-based action recognition methods lose temporal information during data transformation or suffer from noise and outliers caused by sensor imperfections or environmental factors. To address these challenges, we propose a novel framework that preserves and exploits the spatiotemporal structure of event data for action recognition. Our framework consists of two main components: 1) a point-wise event masked autoencoder (MAE) that learns a compact and discriminative representation of event patches by reconstructing them from masked raw event camera points data; 2) an improved event points patch generation algorithm that leverages an event data inlier model and point-wise data augmentation techniques to enhance the quality and diversity of event points patches. To the best of our knowledge, our approach introduces the pre-train method into event camera raw points data for the first time, and we propose a novel event points patch embedding to utilize transformer-based models on event cameras.

关键词： visualization Autoencoders Speech recognition Vision sensors Cameras Transformers data models Spatiotemporal phenomena Voltage control Speech processing

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SentiFormer: Metadata Enhanced Transformer for Image Sentiment Analysis

SentiFormer: Metadata Enhanced Transformer for Image Sentime...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Bin Feng Shulan Ruan Mingzheng Yang Dongxuan Han Huijie Liu Kai Zhang Qi Liu University of Science and Technology of China State Key Laboratory of Cognitive Intelligence Shenzhen International Graduate School Tsinghua University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

As more and more internet users post images online to express their daily emotions, image sentiment analysis has attracted increasing attention. Recently, researchers generally tend to design different neural networks to extract visual features from images for sentiment analysis. Despite the significant progress, metadata, the data (e.g., text descriptions and keyword tags) for describing the image, has not been sufficiently explored in this task. In this paper, we propose a novel Metadata Enhanced Transformer for sentiment analysis (SentiFormer) to fuse multiple metadata and the corresponding image into a unified framework. Specifically, we first obtain multiple metadata of the image and unify the representations of diverse data. To adaptively learn the appropriate weights for each metadata, we then design an adaptive relevance learning module to highlight more effective information while suppressing weaker ones. Moreover, we further develop a cross-modal fusion module to fuse the adaptively learned representations and make the final prediction. Extensive experiments on three publicly available datasets demonstrate the superiority and rationality of our proposed method.

关键词： Sentiment analysis visualization Analytical models Fuses Neural networks Metadata Signal processing Transformers Feature extraction Speech processing

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A Comparative Analysis of data Resampling Techniques for Addressing Class Imbalance in Mosquito Classification

A Comparative Analysis of Data Resampling Techniques for Add...

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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE conference on

作者： Reshma Pise Abhishek Singh Chauhan Bharati Ainapure Aditya Shenolkar Leena Deshpande Shivraj Jadhav Computer Engineering Dept. Vishwakarma University Pune India CE (Software Engineering) Dept. Vishwakarma Institute of Information Technology Pune India

ISBN: (数字)9798331521691

ISBN: (纸本)9798331521707

Mosquitoes, as vectors of life-threatening diseases such as dengue, Zika, and West Nile virus, present a significant public health challenge. Precise and rapid identification of mosquito species is crucial for early detection and prevention of these diseases. Deep learning techniques have proven to be highly effective for automatic visual insect classification tasks. However, the imbalanced distribution of mosquito species within training datasets severely impacts the algorithm’s classification performance. This study investigates data sampling solutions for learning from imbalanced mosquito images dataset. We transfer-learn InceptionV3, a pre-trained convolutional neural network to classify images of two vector mosquito species Aedes Aegypti and Anopheles stephensi. The effectiveness of these data sampling solutions is evaluated and compared in terms of their impact on the performance of CNN classifier.

关键词： Training visualization Technological innovation Accuracy Sensitivity Prevention and mitigation Vectors Convolutional neural networks Viruses (medical) Diseases

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Integrating Failures in Robot Skill Acquisition with Offline Action-Sequence Diffusion RL

Integrating Failures in Robot Skill Acquisition with Offline...

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international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Hecheng Wang Lizhe Qi Yunquan Sun Academy for Engineering & Technology Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recent advancements in robot learning leverage large language models (LLMs) and sampling-based task and motion planning (TAMP) modules for automatic and scalable robot data generation. This method yields both success trajectories and a large number of failure trajectories. Prior works typically filter out failure data and adopt behavior cloning (BC) to train policy. However, this significantly reduces the sample efficiency of the method and results in a policy limited by the data collection behavior policy. In this paper, we introduce a vision-language-conditioned action-sequence diffusion policy and an action-sequence diffusion policy learning with Q-guided refinement for its training. We first redefine the reverse process of the diffusion model as the distribution of action sequences conditioned on visual observations and language instructions. We then employ BC to pretrain the policy on the success sub-dataset. Next, we optimize the action-sequence Q-value function by minimizing the temporal difference error across the complete dataset. Finally, we integrate guidance from the Q-value function into the BC loss of the reverse diffusion chain. Our method significantly outperforms baseline methods in terms of success rate and sample efficiency. By effectively leveraging failure data to optimize the policy, our method can achieve results comparable to those trained with the complete success sub-dataset while requiring 20%-30% less success data.

关键词： Training visualization Uncertainty Large language models data collection Signal processing Robot learning Trajectory Planning Speech processing

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Event visualization with Unity in BESIII experiment 42

Event Visualization with Unity in BESIII experiment

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42nd international conference on High Energy Physics, ICHEP 2024

作者： Li, Jingshu You, Zhengyun Sun Yst-sen University No.135 West Xingang Road Guangzhou China

In the realm of high-energy physics experiments, the ability of software to visualize data plays a pivotal role. It supports the design of detectors, aids in data processing, and enhances the potential to refine physics analysis. The integration of complex detector geometry and structures, using formats such as GDML or ROOT, into systems like Unity for 3D modeling is a key aspect of this process. This research employs Unity to render BESIII spectrometer and events in three-dimensional animated format. Such visual representations of events effectively demonstrate the particle collisions and trajectory interactions with the detector. The development of the visualization system for event displays through software not only improves physics analysis, but also encourages cross-disciplinary applications and contributes to educational initiatives. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 international License (CC BY-NC-ND 4.0).

关键词： data integration

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Intelligent Agricultural Greenhouse data Acquisition and Transmission and Equipment Control System Based on IOT

Intelligent Agricultural Greenhouse Data Acquisition and Tra...

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3rd international conference on Internet of Things, Communication and Intelligent Technology, IoTCIT 2024

作者： Qiu, Jiejin Chen, Zhenwen Qi, Wang Chen, Xiaoyi Huang, Zhanghan Meng, Yuexin Mu, Guangrui Bi, Lvqing College of Physics and Telecommunication Engineering Yulin Normal University Guangxi Yulin China Guangxi Center for Applied Mathematics Guangxi Colleges and Universities Key Laboratory of Complex System Optimization and Big Data Processing Yulin Normal University Yulin China

ISBN: (纸本)9789819627660

With the popularization of Internet of Things technology, smart agriculture is gradually becoming a new driving force to promote the development of agricultural modernization. Aiming at environmental detection and equipment control of agricultural greenhouses, this paper introduces a data acquisition, transmission and equipment control system based on Internet of Things technology. The system uses sensors to collect various environmental parameters in the greenhouse, and realizes the wireless upload of data to Baidu Cloud platform through intelligent judgment, and presents it in a visual way. Through Web technology, the equipment in the greenhouse can be controlled remotely. The test shows that the system integrates intelligent control, real-time data transmission and visualization functions, and uses IOT, automation and MQTT protocol network technology to achieve automatic collection, analysis and feedback of environmental parameters, and complete data wireless transmission, visualization and human–computer interaction. This provides an efficient and convenient solution for the management of smart agricultural greenhouses. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental testing

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Using Convolutional Neural Networks for Image Recognition in IoT Applications

Using Convolutional Neural Networks for Image Recognition in...

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Pervasive Computational Technologies (ICPCT), international conference on

作者： Durgam Rajababu Sampenga Veerraju K. Suresh Kumar Sonali Gupta V B Thurai Raaj Rahul Joshi Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communications Engineering Dadi Institute of Engineering and Technology Anakapalli Visakhapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Poonamallee Chennai J C Bose University of Science and Technology YMCA Faridabad Electrical and Electronics Engineering Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh Department of Journalism & Mass Communication SMeH Manav Rachna International Institute of Research and Studies Haryana India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

Convolutional Neural Networks (CNNs) have grown into a powerful picture identification tool. They are an important part of making Internet of Things (IoT) apps. In this regard, CNNs make it possible to analyse visual data gathered from networked devices, like cameras and sensors, effectively and accurately. With an emphasis on its potential for real-time applications for example smart surveillance, healthcare monitoring, autonomous cars, and industrial automation, this study investigates the incorporation of CNN-based image identification into IoT contexts. We tackle important issues such as the requirement for low-latency processing, energy economy, and computing limitations on edge devices. In order to maximise CNN performance in the resource-constrained IoT ecosystem, strategies like model compression, edge computing, and distributed architectures are covered. The ability to interpret large volumes of visual data is improved by the integration of CNNs with IoT, providing creative solutions for automation and intelligent decision-making across numerous industries.

关键词： Training visualization Automation Computational modeling Surveillance Training data Feature extraction Convolutional neural networks Internet of Things Image classification

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