BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference最新文献

Learning Anatomically Consistent Embedding for Chest Radiography. 学习胸部放射摄影的解剖一致性嵌入

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-11-01

Ziyu Zhou, Haozhe Luo, Jiaxuan Pang, Xiaowei Ding, Michael Gotway, Jianming Liang

{"title":"Learning Anatomically Consistent Embedding for Chest Radiography.","authors":"Ziyu Zhou, Haozhe Luo, Jiaxuan Pang, Xiaowei Ding, Michael Gotway, Jianming Liang","doi":"","DOIUrl":"","url":null,"abstract":"<p><p>Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images. Compared with photographic images, medical images acquired with the same imaging protocol exhibit high consistency in anatomy. To exploit this anatomical consistency, this paper introduces a novel SSL approach, called PEAC (patch embedding of anatomical consistency), for medical image analysis. Specifically, in this paper, we propose to learn global and local consistencies via stable grid-based matching, transfer pre-trained PEAC models to diverse downstream tasks, and extensively demonstrate that (1) PEAC achieves significantly better performance than the existing state-of-the-art fully/self-supervised methods, and (2) PEAC captures the anatomical structure consistency across views of the same patient and across patients of different genders, weights, and healthy statuses, which enhances the interpretability of our method for medical image analysis. All code and pretrained models are available at GitHub.com/JLiangLab/PEAC.</p>","PeriodicalId":72437,"journal":{"name":"BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference","volume":"2023 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11135486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141174538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single Pixel Spectral Color Constancy 单像素光谱色彩常数

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-09-01 DOI: 10.1007/s11263-023-01867-x

S. Koskinen, Erman Acar, Joni-Kristian Kämäräinen

引用次数: 0

DiffSketching: Sketch Control Image Synthesis with Diffusion Models DiffSketching:用扩散模型合成草图控制图像

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-05-30 DOI: 10.48550/arXiv.2305.18812

Qiang Wang, Di Kong, Fengyin Lin, Yonggang Qi

引用次数: 4

Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation 缺陷转移GAN:用于数据增强的多种缺陷综合

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-02-16 DOI: 10.48550/arXiv.2302.08366

Ruyu Wang, Sabrina Hoppe, Eduardo Monari, Marco F. Huber

{"title":"Defect Transfer GAN: Diverse Defect Synthesis for Data Augmentation","authors":"Ruyu Wang, Sabrina Hoppe, Eduardo Monari, Marco F. Huber","doi":"10.48550/arXiv.2302.08366","DOIUrl":"https://doi.org/10.48550/arXiv.2302.08366","url":null,"abstract":"Data-hunger and data-imbalance are two major pitfalls in many deep learning approaches. For example, on highly optimized production lines, defective samples are hardly acquired while non-defective samples come almost for free. The defects however often seem to resemble each other, e.g., scratches on different products may only differ in a few characteristics. In this work, we introduce a framework, Defect Transfer GAN (DT-GAN), which learns to represent defect types independent of and across various background products and yet can apply defect-specific styles to generate realistic defective images. An empirical study on the MVTec AD and two additional datasets showcase DT-GAN outperforms state-of-the-art image synthesis methods w.r.t. sample fidelity and diversity in defect generation. We further demonstrate benefits for a critical downstream task in manufacturing -- defect classification. Results show that the augmented data from DT-GAN provides consistent gains even in the few samples regime and reduces the error rate up to 51% compared to both traditional and advanced data augmentation methods.","PeriodicalId":72437,"journal":{"name":"BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference","volume":"7 1","pages":"445"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89106587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Mitigating Bias in Visual Transformers via Targeted Alignment 通过目标对齐减轻视觉变形中的偏差

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-02-08 DOI: 10.48550/arXiv.2302.04358

Sruthi Sudhakar, Viraj Prabhu, Arvindkumar Krishnakumar, Judy Hoffman

引用次数: 4

Program Generation from Diverse Video Demonstrations 从不同的视频演示程序生成

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-02-01 DOI: 10.48550/arXiv.2302.00178

Anthony Manchin, J. Sherrah, Qi Wu, A. Hengel

{"title":"Program Generation from Diverse Video Demonstrations","authors":"Anthony Manchin, J. Sherrah, Qi Wu, A. Hengel","doi":"10.48550/arXiv.2302.00178","DOIUrl":"https://doi.org/10.48550/arXiv.2302.00178","url":null,"abstract":"The ability to use inductive reasoning to extract general rules from multiple observations is a vital indicator of intelligence. As humans, we use this ability to not only interpret the world around us, but also to predict the outcomes of the various interactions we experience. Generalising over multiple observations is a task that has historically presented difficulties for machines to grasp, especially when requiring computer vision. In this paper, we propose a model that can extract general rules from video demonstrations by simultaneously performing summarisation and translation. Our approach differs from prior works by framing the problem as a multi-sequence-to-sequence task, wherein summarisation is learnt by the model. This allows our model to utilise edge cases that would otherwise be suppressed or discarded by traditional summarisation techniques. Additionally, we show that our approach can handle noisy specifications without the need for additional filtering methods. We evaluate our model by synthesising programs from video demonstrations in the Vizdoom environment achieving state-of-the-art results with a relative increase of 11.75% program accuracy on prior works","PeriodicalId":72437,"journal":{"name":"BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference","volume":"82 1","pages":"1039"},"PeriodicalIF":0.0,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79962771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Local Feature Extraction from Salient Regions by Feature Map Transformation 基于特征映射变换的显著区域局部特征提取

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-01-25 DOI: 10.48550/arXiv.2301.10413

Yerim Jung, Nur Suriza Syazwany, Sang-Chul Lee

引用次数: 2

Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network 基于支持诱导图卷积网络的少镜头语义分割

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-01-09 DOI: 10.48550/arXiv.2301.03194

Jie Liu, Yanqi Bao, Wenzhe Yin, Haochen Wang, Yang Gao, J. Sonke, E. Gavves

{"title":"Few-shot Semantic Segmentation with Support-induced Graph Convolutional Network","authors":"Jie Liu, Yanqi Bao, Wenzhe Yin, Haochen Wang, Yang Gao, J. Sonke, E. Gavves","doi":"10.48550/arXiv.2301.03194","DOIUrl":"https://doi.org/10.48550/arXiv.2301.03194","url":null,"abstract":"Few-shot semantic segmentation (FSS) aims to achieve novel objects segmentation with only a few annotated samples and has made great progress recently. Most of the existing FSS models focus on the feature matching between support and query to tackle FSS. However, the appearance variations between objects from the same category could be extremely large, leading to unreliable feature matching and query mask prediction. To this end, we propose a Support-induced Graph Convolutional Network (SiGCN) to explicitly excavate latent context structure in query images. Specifically, we propose a Support-induced Graph Reasoning (SiGR) module to capture salient query object parts at different semantic levels with a Support-induced GCN. Furthermore, an instance association (IA) module is designed to capture high-order instance context from both support and query instances. By integrating the proposed two modules, SiGCN can learn rich query context representation, and thus being more robust to appearance variations. Extensive experiments on PASCAL-5i and COCO-20i demonstrate that our SiGCN achieves state-of-the-art performance.","PeriodicalId":72437,"journal":{"name":"BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference","volume":"40 1","pages":"126"},"PeriodicalIF":0.0,"publicationDate":"2023-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74616690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

RGB-T Multi-Modal Crowd Counting Based on Transformer 基于变压器的RGB-T多模态人群计数

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-01-08 DOI: 10.48550/arXiv.2301.03033

Zhengyi Liu, liuzywen

引用次数: 2

MoBYv2AL: Self-supervised Active Learning for Image Classification MoBYv2AL:用于图像分类的自监督主动学习

BMVC : proceedings of the British Machine Vision Conference. British Machine Vision Conference Pub Date : 2023-01-04 DOI: 10.48550/arXiv.2301.01531

Razvan Caramalau, Binod Bhattarai, D. Stoyanov, Tae-Kyun Kim

引用次数: 0