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Sparse Multimodal Vision Transformer for Weakly Supervised Semantic Segmentation

Sparse Multimodal Vision Transformer for Weakly Supervised S...

引用

2023 ieee/CVF conference on computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Hanna, Joëlle Mommert, Michael Borth, Damian University of St.Gallen AIML Lab School of Computer Science Switzerland

ISBN: (纸本)9798350302493

Vision Transformers have proven their versatility and utility for complex computer vision tasks, such as land cover segmentation in remote sensing applications. While performing on par or even outperforming other methods like Convolutional Neural Networks (CNNs), Transformers tend to require even larger datasets with fine-grained annotations (e.g., pixel-level labels for land cover segmentation). To overcome this limitation, we propose a weakly-supervised vision Transformer that leverages image-level labels to learn a semantic segmentation task to reduce the human annotation load. We achieve this by slightly modifying the architecture of the vision Transformer through the use of gating units in each attention head to enforce sparsity during training and thereby retaining only the most meaningful heads. This allows us to directly infer pixel-level labels from image-level labels by post-processing.the un-pruned attention heads of the model and refining our predictions by iteratively training a segmentation model with high fidelity. Training and evaluation on the DFC2020 dataset show that our method1 not only generates high-quality segmentation masks using image-level labels, but also performs on par with fully-supervised training relying on pixel-level labels. Finally, our results show that our method is able to perform weakly-supervised semantic segmentation even on small-scale datasets. © 2023 ieee.

关键词： Semantic Segmentation

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Research on computer Vision in Object Detection

Research on Computer Vision in Object Detection

引用

2022 International conference on Mathematics, Modeling, and computer Science, MMCS 2022

作者： Wei, Xiang Ren, Youli Huang, Mian Yunnan Land and Resources Vocational College Yunnan China

ISBN: (纸本)9781510664593

Object detection is a hot topic in the field of machine vision. In the innovation and development of modern technology, the integration of machine learning, pattern recognition, image processing.and other multidisciplinary knowledge can effectively solve the image problem. In computer vision and digital image processing.technology in the development of innovation, target detection theory research and practical application has achieved excellent results, although there are many problems need to solve, but a lot of new technology and new method has yet to be developed, so countries scholars gradually strengthen the scientific research strength, strong adaptability are put forword technique, high precision and good robustness. In this paper, based on the understanding of the research status of object detection, according to the basic theory of image processing. the application of computer vision in object detection is analyzed. The final experimental results show that shadow detection, image enhancement and filtering methods can effectively improve the application effect. © 2023 SPIE.

关键词： Object detection

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A Comparative Analysis of Liver Cancer Detection using Neural Network Algorithm and Correlation Algorithm 2

A Comparative Analysis of Liver Cancer Detection using Neura...

引用

2nd International conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024

作者： Srivastava, Deepak Sharma, Vibhor Kumar, Pramod Sharma, Yogesh Kumar Swami Rama Himalayan University Himalayan School of Science & Technology Department of Computer Science & Engineering Dehradun India I.T.S. Engineering College Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350364699

The fast development of machine learning and AI in recent years has made it a widely used method. Because of its widespread use and effective pattern recognition and classification techniques, big institutions have begun funding research in the healthcare sector to boost the precision with which illnesses may be predicted. However, there are a number of difficulties in applying these methods. A significant barrier is the lack of a sufficient medical picture data set. This paper aims to provide a modest introduction to deep learning in medical image processing. including both the theoretical foundations and practical implementations of this technique. This thesis explores various major advances in computer science that contribute to the widespread appeal of deep learning (DL). This sheds light on the causes behind deep learning's meteoric rise in numerous application fields, including medical image processing. The aim was to deploy new methodologies on existing cancer datasets to see if machine learning and deep learning algorithms might be used for the detection of cancer. The purpose of this study was to classify cancer using many text and picture datasets. Liver BUPA disease database is the dataset that is processed using various machine learning and deep learning models. In this paper, we provide optimization algorithms that outperform the existing strategies in terms of efficiency. © 2024 ieee.

关键词： Contrastive Learning

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Connecting NeRFs, images, and Text

Connecting NeRFs, Images, and Text

引用

ieee computer society conference on computer Vision and pattern recognition Workshops (CVPRW)

作者： Francesco Ballerini Pierluigi Zama Ramirez Roberto Mirabella Samuele Salti Luigi Di Stefano University of Bologna

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Neural Radiance Fields (NeRFs) have emerged as a standard framework for representing 3D scenes and objects, introducing a novel data type for information exchange and storage. Concurrently, significant progress has been made in multimodal representation learning for text and image data. This paper explores a novel research direction that aims to connect the NeRF modality with other modalities, similar to established methodologies for images and text. To this end, we propose a simple framework that exploits pre-trained models for NeRF representations alongside multimodal models for text and image processing. Our framework learns a bidirectional mapping between NeRF embeddings and those obtained from corresponding images and text. This mapping unlocks several novel and useful applications, including NeRF zero-shot classification and NeRF retrieval from images or text.

关键词： Representation learning computer vision Three-dimensional displays image processing conferences Neural radiance field pattern recognition

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Light in the Dark: Cooperating Prior Knowledge and Prompt Learning for Low-Light Action recognition 31

Light in the Dark: Cooperating Prior Knowledge and Prompt Le...

引用

31st ieee International conference on image processing.Challenges and Workshops, ICIPCW 2024

作者： Liu, Qi Ye, Shu Wuhan University of Technology Hubei Key Lab of Transportation Internet of Things School of Computer Science and Artificial Intelligence China

ISBN: (纸本)9798331515942

Low-light environments present a significant challenge for action recognition. Due to insufficient lighting, video frames exhibit reduced detail and contrast, lacking the rich illumination information characteristic of standard RGB videos. Additionally, the dynamic range of cameras in low-light conditions is limited, making it difficult to capture changes in the video scenes. Thus, capturing motion information from low-light videos presents a significant challenge. We introduce a low-light action recognition model named Prior-knowledge Enhancement Prompt-learning Network (PEPN), which integrates video features based on illumination-invariant priors with large model motion description prompts. Unlike conventional methods, our method builds a feature bridge between low-light and standard videos through the use of illumination-invariant priors, addressing the issue of capturing lighting features in traditionally challenging low-light environments. Moreover, we enhance motion information using action descriptions. Specifically, we employ a pre-Trained large-scale language model as a knowledge engine to enhance lighting information with illumination-invariant priors and propose a multimodal training scheme. Furthermore, we align action descriptions with action features through joint understanding of video and text. We introduce an action-specific video enhancement module for priors with RGB features, which demonstrates superior performance on DARK-48 and INFAR datasets compared to previous models. © 2024 ieee.

关键词： Motion capture

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PsSR: Hybrid Path Selection Mechanism for Efficient image Super-Resolution

PsSR: Hybrid Path Selection Mechanism for Efficient Image Su...

引用

2025 ieee International conference on Acoustics, Speech, and Signal processing. ICASSP 2025

作者： Ding, Cheng Zhao, ZhongQiu Zhao, Yang Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350368741

In practical applications, large images are processed in patches, many of which are simple and smooth, making them suitable for lighter network processing. This paper proposes a hybrid path selection mechanism that enables the dynamic routing of patches through the network, achieving a balance between accuracy and computational efficiency. The proposed method develops a dual-branch architecture, in which the two branches keep the same structural design, but differ in the number of channels. An efficient classification module is integrated for directing patches to the appropriate branches within the model. In addition, within each branch, we introduce an early exit strategy to further reduce the calculation cost. Subsequently, we enhance the restoration capabilities of the lightweight branch through channel and spatial feature distillation. Experimental results demonstrate that our Hybrid Path Selection based Super-Resolution (PsSR) mechanism effectively reduces computational costs while preserving the integrity of the backbone performance. © 2025 ieee.

关键词： dual-branch knowledge distillation path selection super-resolution

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Hyperparameter Tuning of Identity Block Uses an Imbalance Dataset with Hyperband Method 18

Hyperparameter Tuning of Identity Block Uses an Imbalance Da...

引用

18th International conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Manga, Abdul Rachman Latief, Muhammad Acqmal Fadhilla Gaffar, Andi Widya Mufila Azis, Huzain Satra, Ramdan Salim, Yulita Universitas Muslim Indonesia Faculty of Computer Science Makassar Indonesia

ISBN: (纸本)9798350331011

Visual pattern recognition, selection of appropriate image processing.techniques, and network architecture are key factors in achieving optimal model performance. This article focuses on the application of Identity Blocks in the context of image processing. especially on unbalanced datasets. Three different datasets, namely Plant Diseases, Rock & Paper Scissors, and Animal Faces, are used in this study, each with unique characteristics. Identity Block, implemented in the ResNet network architecture, helps to overcome the gradient loss problem that often occurs in deep neural networks (DNN) with deep layers. This research specifically explores Identity Block optimization using the hyperband method to improve model performance. The average performance improvement of all optimized models is 4.45% in accuracy, 5.39% in precision, 6.4% in recall, and 6.48% in F1-score. These results show that model optimization is very good at improving identity block performance using the hyperband method. © 2024 ieee.

关键词： Network architecture

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Single-image depth estimation based on multi-scale feature fusion

Single-image depth estimation based on multi-scale feature f...

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2024 International conference on image processing.and Artificial Intelligence, ICIPAl 2024

作者： Wang, Handong He, Lixin Zhou, Chengying Yang, Jing Cheng, Zhi Cao, Shenjie Collaborative Innovation Centre for Computer Vision and Pattern Recognition School of Artificial Intelligence and Big Data Hefei University Hefei230601 China School of Electronic and Information Engineering Anhui Jianzhu University No. 292 Ziyun Road Hefei230009 China

ISBN: (纸本)9781510681514

Begin Single-image depth estimation holds significant application value in the field of computer vision. Traditional methods have certain limitations in accurately locating distant regions in images and predicting the precision of object edges. Therefore, this paper proposes a depth estimation method from a single image using fusion of multi-scale features. Firstly, we construct a feature extraction module that combines Transformer and multi-scale feature fusion mechanisms. This module effectively captures features at different scales and levels in images while focusing on global features, resulting in a more comprehensive and precise feature representation. Secondly, we design a Scene Feature Encoding Module that utilizes dilated convolution blocks and adaptive average pooling to extract context correlations between pixels in images, enabling effective recognition of distant regions. Finally, we design a Strip Spatial Perception Module to refine the perception of depth changes, thereby enhancing overall estimation accuracy. Experimental results showcase the outstanding performance of this approach across diverse scenarios, proving its practicality and wide-ranging application prospects in the field of depth estimation from a single image. © 2024 SPIE.

关键词： image fusion

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Enhancing Daily Reports: Application of Multimodal GPT for image Description Generation in Construction Site Quality Assurance 31

Enhancing Daily Reports: Application of Multimodal GPT for I...

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31st ieee International conference on image processing.Challenges and Workshops, ICIPCW 2024

作者： Chang, Chuan-Yu Khanum, Abida Hsu, Hui-Huang Tsai, Ming-Hsiu Chen, Wei-Ting Su, Sheng-Gui National Yunlin University of Science and Technology Intelligent Recognition Industry Service Research Center Taiwan Tamkang University Department of Computer Science Information Engineering Taiwan Tamkang University Department of Civil Engineering Taiwan

ISBN: (纸本)9798331515942

In recent years, cutting-edge language models like GPT, TAIDE, LLaMa, and VLM have exhibited remarkable prowess across diverse natural language tasks. This surge in artificial intelligence and computer vision technologies has revolutionized image analysis, offering a fresh and viable approach. Yet, the management and analysis of construction site images persist as formidable challenges in construction projects. While these images harbor valuable insights, conventional manual analysis methods are plagued by inefficiency and error susceptibility. Our study introduces an innovative multimodal approach named Daily Report for Construction Site GPT (DrCS-GPT), designed to navigate the intricacies of building construction sites. The study was initiated by compiling a diverse dataset encompassing visual images and reports from building construction sites in collaboration with CECI Engineering Consultants, Inc., Taiwan. In the data acquisition, the focus shifted towards refining our proposed model through pre-Training and fine-Tuning processes. This model exhibits proficiency in generating high-quality textual content while addressing common issues such as fragmented sentences and word repetition. The methodology entailed dataset assembly, model refinement, and integration with advanced linguistic tools to ensure precision and coherence in scientific communication. Our results underscore the model's exceptional performance on 1600 images, with inference times of just 0.21s per task. © 2024 ieee.

关键词： Visual languages

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Analysis of Facial Expression using Deep Learning Techniques

Analysis of Facial Expression using Deep Learning Techniques

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2023 ieee International conference on Integrated Circuits and Communication Systems, ICICACS 2023

作者： Patil, Priyadarshini C. Ashwin, R.K. Kumar G, Arvind Bhaskar, M. Rajesh, N. Pda College of Engineering Department of Computer Science & Engineering Kalaburagi India H. K. E. Society's S. L. N. College of Engineering Department of Computer Science & Engineering Raichur India Ramaiah Institute of Technology Department of Electronics and Telecommunication Engineering Bengaluru India K L University Vadeeshwaram Department of Computer Science & Engineering Vijayawada India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350398465

Research into facial recognition has been one of the most intriguing and extensive areas of study for decades. Because of its importance in reading and conveying other people's emotions, the face often becomes the focal point of conversations. A facial recognition system's ability to identify an individual from a digital image or video is useful for a wide variety of security-related purposes, including surveillance, general identity verification, criminal-justice Systems, image database investigations, Smart Card applications, multi-media settings with adaptive human-computer interfaces, video indexing, gender classification, facial feature recognition, and tracking. While being less reliable than fingerprint and iris identification, face recognition has seen widespread adoption because of its contactless, non-invasive nature. Light, emotion, and posture all have a role in making it harder to recognise a person's face. A computer model of face recognition is difficult to build because of the problem space's complexity and possible multidimensionality. Extensive attempts have been made to build accurate and reliable face recognition systems. The high dimensionality of a facial image is necessary for detecting minute variations in features, but this also means that the calculations required to classify the image take a long time. It's possible that lowering the image resolution will reduce the time needed for recognition processing. Facial recognition systems have been studied for quite some time, but there is always opportunity for improvement. These results show that current face recognition algorithms have matured to a considerable extent while operating in a constrained context. When put to the test in the real world, however, these technologies do not show a significant performance boost in all of the frequent cases faced by applications. Genetic face recognition is necessary when only the most cutting-edge method of facial identification will do. There are many

关键词： Face recognition

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