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Patch-level Representation Learning for Self-supervised Vision Transformers

Patch-level Representation Learning for Self-supervised Visi...

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

作者： Yun, Sukmin Lee, Hankook Kim, Jaehyung Shin, Jinwoo Korea Adv Inst Sci & Technol KAIST Seoul South Korea

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent self-supervised learning (SSL) methods have shown impressive results in learning visual representations from unlabeled images. This paper aims to improve their performance further by utilizing the architectural advantages of the underlying neural network, as the current state-of-the-art visual pretext tasks for SSL do not enjoy the benefit, i.e., they are architecture-agnostic. In particular, we focus on Vision Transformers (ViTs), which have gained much attention recently as a better architectural choice, often outperforming convolutional networks for various visual tasks. The unique characteristic of ViT is that it takes a sequence of disjoint patches from an image and processes patch-level representations internally. Inspired by this, we design a simple yet effective visual pretext task, coined SelfPatch, for learning better patch-level representations. To be specific, we enforce invariance against each patch and its neighbors, i.e., each patch treats similar neighboring patches as positive samples. Consequently, training ViTs with SelfPatch learns more semantically meaningful relations among patches (without using human-annotated labels), which can be beneficial, in particular, to downstream tasks of a dense prediction type. Despite its simplicity, we demonstrate that it can significantly improve the performance of existing SSL methods for various visual tasks, including object detection and semantic segmentation. Specifically, SelfPatch significantly improves the recent self-supervised ViT DINO, by achieving +1.3 AP on COCO object detection, +1.2 AP on COCO instance segmentation, and +2.9 mIoU on ADE2OK semantic segmentation.

关键词： Training Representation learning visualization Neural networks Object detection Self-supervised learning Transformers

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Deep Hierarchical Semantic Segmentation

Deep Hierarchical Semantic Segmentation

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

作者： Li, Liulei Zhou, Tianfei Wang, Wenguan Li, Jianwu Yang, Yi Beijing Inst Technol Beijing Peoples R China Swiss Fed Inst Technol Zurich Switzerland Univ Technol Sydney AAII ReLER Sydney NSW Australia Zhejiang Univ CCAI Hangzhou Peoples R China Baidu Res Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Humans are able to recognize structured relations in observation, allowing us to decompose complex scenes into simpler parts and abstract the visual world in multiple levels. However, such hierarchical reasoning ability of human perception remains largely unexplored in current literature of semantic segmentation. Existing work is often aware of flatten labels and predicts target classes exclusively for each pixel. In this paper, we instead address hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise description of visual observation in terms of a class hierarchy. We devise H SSN , a general HSS framework that tackles two critical issues in this task: i) how to efficiently adapt existing hierarchy-agnostic segmentation networks to the HSS setting, and ii) how to leverage the hierarchy information to regularize HSS network learning. To address i), HSSN directly casts HSS as a pixel-wise multi-label classification task, only bringing minimal architecture change to current segmentation models. To solve ii), HSSN first explores inherent properties of the hierarchy as a training objective, which enforces segmentation predictions to obey the hierarchy structure. Further, with hierarchy-induced margin constraints, HSSNreshapes the pixel embedding space, so as to generate well-structured pixel representations and improve segmentation eventually. We conduct experiments on four semantic segmentation datasets (i.e., Mapillary Vistas 2.0, City-scapes, LIP, and PASCAL-Person-Part), with different class hierarchies, segmentation network architectures and backbones, showing the generalization and superiority of HSSN.

关键词： Training visualization Computer vision Shape Lips Semantics Network architecture

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visualization Challenges of Variant Interpretation in Multiscale NGS Data

Visualization Challenges of Variant Interpretation in Multis...

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24th Eurographics conference on visualization, EuroVis 2022

作者： Ståhlbom, E. Molin, J. Lundström, C. Ynnerman, A. Division of Media and Information Technology Linköping University Sweden Sectra AB Sweden Center for Medical Image Science and Visualization Linköping University Sweden

ISBN: (纸本)9783038681854

There is currently a movement in health care towards precision medicine, where genomics often is the central diagnostic component for tailoring the treatment to the individual patient. We here present results from a domain characterization effort to pinpoint problems and possibilities for visualization of genomics data in the clinical workflow, with analysis of copy number variants as an example task. Five distinct characteristics have been identified. Clinical genomics data is inherently multiscale, riddled with artifacts and uncertainty, and many findings have unknown significance, so it is a challenging visual analytics domain. Moreover, as in other clinical domains, high efficiency is key. This characterization will form the basis for follow-on visualization prototyping. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： visualization

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ReSTR: Convolution-free Referring Image Segmentation Using Transformers

ReSTR: Convolution-free Referring Image Segmentation Using T...

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

作者： Kim, Namyup Kim, Dongwon Lan, Cuiling Zeng, Wenjun Kwak, Suha POSTECH Pohang South Korea Microsoft Res Asia Beijing Peoples R China EIT Inst Adv Study Auckland New Zealand

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Referring image segmentation is an advanced semantic segmentation task where target is not a predefined class but is described in natural language. Most of existing methods for this task rely heavily on convolutional neural networks, which however have trouble capturing long-range dependencies between entities in the language expression and are not flexible enough for modeling interactions between the two different modalities. To address these issues, we present the first convolution-free model for referring image segmentation using transformers, dubbed ReSTR. Since it extracts features of both modalities through transformer encoders, it can capture long-range dependencies between entities within each modality. Also, ReSTR fuses features of the two modalities by a self-attention encoder;which enables flexible and adaptive interactions between the two modalities in the fusion process. The fused features are fed to a segmentation module, which works adaptively according to the image and language expression in hand. ReSTR is evaluated and compared with previous work on all public benchmarks, where it outperforms all existing models.

关键词： Image segmentation Adaptation models visualization Semantics Benchmark testing Transformers Feature extraction

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Rethinking Illumination for Person Re-Identification: A Unified View

Rethinking Illumination for Person Re-Identification: A Unif...

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

作者： Xiang, Suncheng You, Guanjie Li, Leqi Guan, Mengyuan Liu, Ting Qian, Dahong Fu, Yuzhuo Shanghai Jiao Tong Univ Shanghai Peoples R China Natl Univ Def Technol Changsha Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

As a fundamental problem in video surveillance, person re-identification (re-ID) contributes a lot to the development of modern metro city. Recently, learning from synthetic data on re-ID task, which benefits from the popularity of synthetic data engine, has achieved remarkable performance in both supervised and unsupervised manner. However, previous researches mainly lay emphasis on employing synthetic data to achieve the state-of-the-art performance with a strong backbone, while neglects to perform quantitative studies on how visual factors affect re-ID system. To facilitate the research in this field, firstly, we manually construct a large-scale synthetic dataset named SynPerson, which has diversified human characters and distinguished attributes with accurate annotations. Secondly, we quantitatively analyze the influence of illumination on re-ID system. To our best knowledge, this is the first attempt to explicitly dissect person re-ID from the aspect of illumination on synthetic dataset. Comprehensive experiments help us have a deeper understanding of the fundamental problems in person re-ID. Furthermore, we will release SynPerson to the community, as part of efforts to alleviate the shortage of large-scale pedestrian dataset of future works(1).

关键词： Training visualization Annotations Smart cities Pose estimation Lighting Video surveillance

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GeoROS: Georeferenced Real-time Orthophoto Stitching with Unmanned Aerial Vehicle

GeoROS: Georeferenced Real-time Orthophoto Stitching with Un...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Gao, Guangze Yuan, Mengke Ma, Zhihao Gu, Jiaming Meng, Weiliang Xu, Shibiao Zhang, Xiaopeng Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Zhejiang Lab Hangzhou Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Simultaneous orthophoto stitching during the flight of Unmanned Aerial Vehicles (UAV) can greatly promote the practicability and instantaneity of diverse applications such as emergency disaster rescue, digital agriculture, and cadastral survey, which is of remarkable interest in aerial photogrammetry. However, the inaccurately estimated camera poses and the intuitive fusion strategy of existing methods lead to misalignment and distortion artifacts in orthophoto mosaics. To address these issues, we propose a Georeferenced Real-time Orthophoto Stitching method (GeoROS), which can achieve efficient and accurate camera pose estimation through exploiting geolocation information in monocular visual simultaneous localization and mapping (SLAM) and fuse transformed images via orthogonality-preserving criterion. Specifically, in the SLAM process, georeferenced tracking is employed to acquire high-quality initial camera poses with a geolocation based motion model and facilitate non-linear pose optimization. Meanwhile, we design a georeferenced mapping scheme by introducing robust geolocation constraints in joint optimization of camera poses and the position of landmarks. Finally, aerial images warped with localized cameras are fused by considering both the orthogonality of camera orientation relative to the ground plane and the pixel centrality to fulfill global orthorectification. Besides, we construct two datasets with global navigation satellite system (GNSS) information of different scenarios and validate the superiority of our GeoROS method compared with state-of-the-art methods in accuracy and efficiency.

关键词： Smart agriculture Global navigation satellite system visualization Simultaneous localization and mapping Tracking Geology Cameras

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AI-Driven Revolution: Effectiveness of Product Ads on Social Media Using Midjourney 6

AI-Driven Revolution: Effectiveness of Product Ads on Social...

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6th International conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Lorenza, Christina Astuty, Eriana Entrepreneurship Department Bina Nusantara University Bandung Indonesia

ISBN: (纸本)9798331539603

The rapid development of artificial intelligence (AI) is inevitable and significantly impacts our lives. Although AI has many positive benefits, some research results show that it cannot fully replicate the complexity and subjectivity of human intelligence and emotions. The research objective is to assess AI's effectiveness in visualizing product advertisements using Midjourney on social media. The research used a quantitative approach with an online questionnaire as an instrument to collect data from the millennial generation and Gen Z in Bandung City from March to June 2024. The results of the descriptive analysis revealed that Gen Z is quite familiar with AI-based advertising and has good knowledge about artificial intelligence and its use. Consequently, Gen Z can easily understand AI-based product advertising messages, leading to increased brand awareness and recall. Overall, most Gen Z responded positively to the Midjourney application as a valuable tool for designers in developing advertising ideas on social media and expressed interest in viewing other product advertising visualizations using AI technology. © 2024 ieee.

关键词： advertisement artificial intelligence (AI) gen Z Midjourney millennials product visualization social media

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Relationship Oriented Semantic Scene Understanding for Daily Manipulation Tasks

Relationship Oriented Semantic Scene Understanding for Daily...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Tang, Chao Yu, Jingwen Chen, Weinan Xia, Bingyi Zhang, Hong Southern Univ Sci & Technol Dept Elect & Elect Engn Shenzhen Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Guangdong Univ Technol BIRL Guangzhou Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

Assistive robot systems have been developed to help people accomplish daily manipulation tasks especially for those with disabilities, where scene understanding plays a crucial role in enabling robots to interpret the surroundings and behave accordingly. Most of the current systems approach scene understanding without considering the functional dependencies between objects. However, it is only valuable to interact with some objects when their function-relevant counterparts are considered. In this paper, we augment an assistive robotic arm system with an end-to-end semantic relationship reasoning model. It incorporates functional relationships between pairs of objects for semantic scene understanding. To ensure good generalization to unseen objects and relationships, the model works in a category-agnostic manner. We evaluate our design and three baseline methods on a self-collected benchmark with two levels of difficulty. To further demonstrate the effectiveness, the model is integrated with a symbolic planner for goaloriented, multi-step manipulation task on a real-world assistive robotic arm platform.

关键词： visualization Semantics Pipelines Benchmark testing Manipulators Assistive robots Cognition

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Multi-stream dynamic video Summarization 22

Multi-stream dynamic video Summarization

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22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Elfeki, Mohamed Wang, Liqiang Borji, Ali Microsoft Albuquerque NM 87108 USA Univ Cent Florida Orlando FL 32816 USA

ISBN: (纸本)9781665409155

With vast amounts of video content being uploaded to the Internet every minute, video summarization becomes critical for efficient browsing, searching, and indexing of visual content. Nonetheless, the spread of social and egocentric cameras creates an abundance of sparse scenarios captured by several devices, and ultimately required to be jointly summarized. In this paper, we discuss the problem of summarizing videos recorded independently by several dynamic cameras that intermittently share the field of view. We present a robust framework that (a) identifies a diverse set of important events among moving cameras that often are not capturing the same scene, and (b) selects the most representative view(s) at each event to be included in a universal summary. Due to the lack of an applicable alternative, we collected a new multi-view egocentric dataset, Multi-Ego. Our dataset is recorded simultaneously by three cameras, covering a wide variety of real-life scenarios. The footage is annotated by multiple individuals under various summarization configurations, with a consensus analysis ensuring a reliable ground truth. We conduct extensive experiments on the compiled dataset in addition to three other standard benchmarks that show the robustness and the advantage of our approach in both supervised and unsupervised settings. Additionally, we show that our approach learns collectively from data of varied number-of-views and orthogonal to other summarization methods, deeming it scalable and generic.

关键词： visualization Computer vision Benchmark testing Cameras Reliability engineering Robustness Internet

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Cross-modal activation of the primary visual cortex by auditory stimulation in RCS rats: considerations in visual prosthesis 44

Cross-modal activation of the primary visual cortex by audit...

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44th Annual International conference of the ieee-Engineering-in-Medicine-and-Biology-Society (EMBC)

作者： Caravaca-Rodriguez, Daniel Suaning, Gregg J. Barriga-Rivera, Alejandro Univ Seville Dept Appl Phys 3 Seville 41092 Spain Univ Sydney Sch Biomed Engn Sydney NSW 2006 Australia

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

An important brain re-wiring, the so-called cross-modal plasticity, occurs during progression of retinal degenerative diseases to compensate for lack of visual input. The visual cortex does not go 'unused', instead it is devoted to processing other sensory modalities. In this study we recorded, in the visual cortex, visual- and auditory-evoked potentials in an anesthetized murine model of retinal degeneration. The latency to the first peak of the recorded local field potentials was used to assess the speed of the response. Visual responses occurred significantly faster in the control group. Conversely, auditory responses appeared significantly faster in animals with retinal degeneration. This suggests the compensatory neural rewiring is optimizing the performance of other sensory modalities, hearing in this case. This phenomenon may play an important role in visual neuro-rehabilitation. Whether or not it can promote or deter the interpretation of artificially encoded neural signals from a visual prosthesis remains to be studied.

关键词： visualization Animals Degenerative diseases Visual prosthesis Auditory system Retina Brain modeling

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