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检索条件"任意字段=IEEE Information Visualization Conference/IEEE Visualization Conference"

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Efficient Two-Stage Detection of Human-Object Interactions with a Novel Unary-Pairwise Transformer

Efficient Two-Stage Detection of Human-Object Interactions w...

引用

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

作者： Zhang, Frederic Z. Campbell, Dylan Gould, Stephen Australian Natl Univ Canberra ACT Australia Univ Oxford Oxford England Australian Ctr Robot Vis Brisbane Qld Australia

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Recent developments in transformer models for visual data have led to significant improvements in recognition and detection tasks. In particular, using learnable queries in place of region proposals has given rise to a new class of one-stage detection models, spearheaded by the Detection Transformer (DETR). Variations on this one-stage approach have since dominated human-object interaction (HOI) detection. However, the success of such one-stage HOI detectors can largely be attributed to the representation power of transformers. We discovered that when equipped with the same transformer, their two-stage counterparts can be more performant and memory-efficient, while taking a fraction of the time to train. In this work, we propose the Unary-Pairwise Transformer, a two-stage detector that exploits unary and pairwise representations for HOIs. We observe that the unary and pairwise parts of our transformer network specialise, with the former preferentially increasing the scores of positive examples and the latter decreasing the scores of negative examples. We evaluate our method on the HICO-DET and V-COCO datasets, and significantly outperform state-of-the-art approaches. At inference time, our model with ResNet50 approaches real-time performance on a single GPU.

关键词： visualization Memory management Graphics processing units Detectors Transformers Real-time systems Pattern recognition

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Multi-Scale Graphical Representation of Cell Environment 44

Multi-Scale Graphical Representation of Cell Environment

引用

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

作者： Theissen, Helen Chakraborty, Tapabrata Malacrino, Stefano Royston, Daniel Rittscher, Jens Univ Oxford Inst Biomed Engn IBME Oxford England Univ Oxford Big Data Inst BDI Dept Engn Sci Oxford England Oxford Univ Hosp NHS Fdn Trust NIHR Oxford Biomed Res Ctr Oxford Oxon England Oxford Univ NHS Fdn Trust John Radcliffe Hosp Dept Cellular Pathol Oxford England

ISBN: (数字)9781728127828

ISBN: (纸本)9781728127828

We present a multi-scale graphical network that can capture the relevant representations of individual cell morphology, topological structure of cell communities in a tissue image, as well as whole slide level attributes. This helps to effectively merge the disease relevant cell morphology to the overall topological context within the sample, within one unified deep framework. From the explainability point of view, instead of empirical design, the graphs are designed with biomedical considerations in mind in order to have translational validity. We also provide a clinically interpretable visualisation of the cells and their micro- and macro-environment by leveraging label noise reduction. We demonstrate the efficacy of our methodology on myeloproliferative neoplasms (MPN), a haematopoietic stem cell disorder as an exemplar test case. The proposed method achieves an encouraging performance in the robust separation of different MPN subtypes in this exciting new dataset as part of this work.

关键词： visualization Network topology Sociology Noise reduction Morphology Stem cells Topology

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SAC: Semantic Attention Composition for Text-Conditioned Image Retrieval 22

SAC: Semantic Attention Composition for Text-Conditioned Ima...

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

作者： Jandial, Surgan Badjatiya, Pinkesh Chawla, Pranit Chopra, Ayush Sarkar, Mausoom Krishnamurthy, Balaji Adobe Media & Data Sci Res Lab San Jose CA 95110 USA Microsoft Hyderabad India Indian Inst Technol Kharagpur W Bengal India MIT Media Lab 77 Massachusetts Ave Cambridge MA 02139 USA Adobe San Jose CA USA

ISBN: (纸本)9781665409155

The ability to efficiently search for images is essential for improving the user experiences across various products. Incorporating user feedback, via multi-modal inputs, to navigate visual search can help tailor retrieved results to specific user queries. We focus on the task of text-conditioned image retrieval that utilizes support text feedback alongside a reference image to retrieve images that concurrently satisfy constraints imposed by both inputs. The task is challenging since it requires learning composite image-text features by incorporating multiple cross-granular semantic edits from text feedback and then applying the same to visual features. To address this, we propose a novel framework SAC which resolves the above in two major steps: "where to see" (Semantic Feature Attention) and "how to change" (Semantic Feature Modification). We systematically show how our architecture streamlines the generation of text-aware image features by removing the need for various modules required by other state-of-art techniques. We present extensive quantitative, qualitative analysis, and ablation studies, to show that our architecture SAC outperforms existing techniques by achieving state-of-the-art performance on 3 benchmark datasets: FashionIQ, Shoes, and Birds-to-Words, while supporting natural language feedback of varying lengths.

关键词： visualization Image resolution Navigation Semantics Image retrieval Natural languages Computer architecture

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Stacked Hybrid-Attention and Group Collaborative Learning for Unbiased Scene Graph Generation

Stacked Hybrid-Attention and Group Collaborative Learning fo...

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

作者： Dong, Xingning Gan, Tian Song, Xuemeng Wu, Jianlong Cheng, Yuan Nie, Liqiang Shandong Univ Jinan Peoples R China Ant Grp Hangzhou Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene Graph Generation, which generally follows a regular encoder-decoder pipeline, aims to first encode the visual contents within the given image and then parse them into a compact summary graph. Existing SGG approaches generally not only neglect the insufficient modality fusion between vision and language, but also fail to provide informative predicates due to the biased relationship predictions, leading SGG far from practical. Towards this end, we first present a novel Stacked Hybrid-Attention network, which facilitates the intra-modal refinement as well as the intermodal interaction, to serve as the encoder. We then devise an innovative Group Collaborative Learning strategy to optimize the decoder. Particularly, based on the observation that the recognition capability of one classifier is limited towards an extremely unbalanced dataset, we first deploy a group of classifiers that are expert in distinguishing different subsets of classes, and then cooperatively optimize them from two aspects to promote the unbiased SGG. Experiments conducted on VG and GQA datasets demonstrate that, we not only establish a new state-of-the-art in the unbiased metric, but also nearly double the performance compared with two baselines. Our code is available at https://***/dongxingning/SHA-GCL-for-SGG.

关键词： Measurement visualization Computer vision Image analysis Codes Computational modeling Pipelines

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RiskRadar: an Online Mapping Platform for Disaster Preparedness and Response via Comprehensive Data Integration and visualization

RiskRadar: an Online Mapping Platform for Disaster Preparedn...

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ieee Green Technologies conference (ieee-Green)

作者： Sunghwan Kim Juho Kim Junhyeong Park Seongyun Ku Minji You Mark Douglas Whitaker School of Engineering & Doerr School of Sustainability Stanford University & Caravan Research Stanford CA USA University of Ghent & Caravan Research Ghent Belgium Stony Brook University & Caravan Research Stony Brook NY USA Graduate School of Business Kyung Hee University Seoul Republic of Korea

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

Updatable and accurate mapping tools are essential for effective disaster management to safeguard public safety before, during, or after disasters. Maps help prioritize where assets are deployed in disasters in efficient ways and are important for compiling data layers about normal environmental risk inequalities of various indicators in general, to locate geographically the most vulnerable populations in order to improve their lives. Central to both kinds of mapping efforts are accessibility and clarity of information, whether in plotting a rapidly evolving natural or human-made hazard in a disaster zone, or for sharing with first responders, or for ongoing daily awareness and thus better administration about regular environmental risk in national populations. The RiskRadar platform prototype improves five shortcomings found regularly in other platforms for disaster risk reduction (DRR) and has more comprehensive data integration and visualization tools with its unique data indexes to enhance disaster preparedness and response. By integrating diverse environmental and urban data sets, RiskRadar shows different kinds of risk easily. From this, it facilitates informed decision-making on priorities and timely actions, while aiding more coordinated awareness and thus coordinated response to risk. This paper examines functionalities of a prototype platform for how it bolsters disaster resilience particularly in contexts desired by the United Nations Office for Disaster Risk Reduction (UNDRR). With a user-friendly interface, RiskRadar lets stakeholders, first responders, and the public at large access the same critical information. Good digitally-accessible maps aid better trust upon which better coordination for disaster management and higher group preparedness for future events can take place.

关键词： Disasters Data visualization Prototypes Disaster management Real-time systems Public security Indexes Stakeholders Risk management Resilience

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Using Discord Conversations as Program Comprehension Aid 30

Using Discord Conversations as Program Comprehension Aid

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30th ieee/ACM International conference on Program Comprehension (ICPC)

作者： Raglianti, Marco Nagy, Csaba Minelli, Roberto Lanza, Michele USI REVEAL Software Inst Lugano Switzerland

ISBN: (纸本)9781450392983

Modern communication platforms used in software development host daily conversations among developers and users about a wide range of topics pertaining to software systems, such as language features, APIs, code artifacts like classes and methods, design patterns, usage examples, code reviews, bug reporting and fixing. Discord servers are one of these virtual community hubs that have seen a steep rise in popularity, as coordination and aggregation means for communities of developers. Although Discord supports filter-based search functionalities, the sheer volume, velocity, and small granularity of single messages make it hard to find useful results, let alone complete discussions revolving around particular themes. One reason is that the concept of a discussion, which we call a conversation, does not exist as an explicit concept. We argue that extracting and analyzing such conversations can be used fruitfully to aid program comprehension. We present an approach that reconstructs the conversations that take place on a software community Discord server, focusing on software-related conversations: Our approach binds the conversations to the discussed artifacts. Leveraging our approach, we built a tool that enables the interactive exploration of the conversations' contents. We illustrate its usefulness through a number of examples that highlight how the insights obtained serve as an additional form of software documentation and program comprehension aid.

关键词： conversations Discord visualization

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DiLiGenT10²: A Photometric Stereo Benchmark Dataset with Controlled Shape and Material Variation

DiLiGenT10<SUP>2</SUP>: A Photometric Stereo Benchmark Datas...

引用

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

作者： Ren, Jieji Wang, Feishi Zhang, Jiahao Zheng, Qian Ren, Mingjun Shi, Boxin Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China Peking Univ Natl Engn Res Ctr Visual Technol Sch Comp Sci Beijing Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Peking Univ Inst Artificial Intelligence Beijing Peoples R China Beijing Acad Artificial Intelligence Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Evaluating photometric stereo using real-world dataset is important yet difficult. Existing datasets are insufficient due to their limited scale and random distributions in shape and material. This paper presents a new real-world photometric stereo dataset with "ground truth" normal maps, which is 10 times larger than the widely adopted one. More importantly, we propose to control the shape and material variations by fabricating objects from CAD models with carefully selected materials, covering typical aspects of reflectance properties that are distinctive for evaluating photometric stereo methods. By benchmarking recent photometric stereo methods using these 100 sets of images, with a special focus on recent learning based solutions, a 10x10 shape-material error distribution matrix is visualized to depict a "portrait" for each evaluated method. From such comprehensive analysis, open problems in this field are discussed. To inspire future research, this dataset is available at https://***.

关键词： Reflectivity visualization Solid modeling Computer vision Shape Benchmark testing Pattern recognition

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SGTR: End-to-end Scene Graph Generation with Transformer

SGTR: End-to-end Scene Graph Generation with Transformer

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

作者： Li, Rongjie Zhang, Songyang He, Xuming ShanghaiTech Univ Sch Informat Sci & Technol Shanghai Peoples R China Shanghai Engn Res Ctr Intelligent Vis & Imaging Shanghai Peoples R China Chinese Acad Sci Shanghai Inst Microsyst & Informat Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up two-stage or a point-based one-stage approach, which often suffers from high time complexity or sub-optimal designs. In this work, wc propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem. To solve the problem, we develop a transformer-based end-to-end framework that first generates the entity and predicate proposal set, followed by inferring directed edges to form the relation triplets. In particular, we develop a new entity-aware predicate representation based on a structural predicate generator that leverages the compositional property of relationships. Moreover, we design a graph assembling module to infer the connectivity of the bipartite scene graph based on our entity-aware structure, enabling us to generate the scene graph in an end-to-end manner. Extensive experimental results show that our design is able to achieve the state-of-the-art or comparable performance on two challenging benchmarks, surpassing most of the existing approaches and enjoying higher efficiency in inference. We hope our model can serve as a strong baseline for the Transformer-based scene graph generation.(1)

关键词： visualization Image analysis Transformers Pattern recognition Decoding Bipartite graph Proposals

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HairCLIP: Design Your Hair by Text and Reference Image

HairCLIP: Design Your Hair by Text and Reference Image

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

作者： Wei, Tianyi Chen, Dongdong Zhou, Wenbo Liao, Jing Tan, Zhentao Yuan, Lu Zhang, Weiming Yu, Nenghai Univ Sci & Technol China Hefei Peoples R China Microsoft Cloud AI Beijing Peoples R China City Univ Hong Kong Hong Kong Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Hair editing is an interesting and challenging problem in computer vision and graphics. Many existing methods require well-drawn sketches or masks as conditional inputs for editing, however these interactions are neither straightforward nor efficient. In order to free users from the tedious interaction process, this paper proposes a new hair editing interaction mode, which enables manipulating hair attributes individually or jointly based on the texts or reference images provided by users. For this purpose, we encode the image and text conditions in a shared embedding space and propose a unified hair editing framework by leveraging the powerful image text representation capability of the Contrastive Language-Image Pre-Training (CLIP) model. With the carefully designed network structures and loss functions, our framework can perform high-quality hair editing in a disentangled manner. Extensive experiments demonstrate the superiority of our approach in terms of manipulation accuracy, visual realism of editing results, and irrelevant attribute preservation.

关键词： Hair Computer vision visualization Costs Machine vision Pattern recognition

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Surpassing the Human Accuracy: Detecting Gallbladder Cancer from USG Images with Curriculum Learning

Surpassing the Human Accuracy: Detecting Gallbladder Cancer ...

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

作者： Basu, Soumen Gupta, Mayank Rana, Pratyaksha Gupta, Pankaj Arora, Chetan Indian Inst Technol Delhi India Postgrad Inst Med Educ & Res Chandigarh India

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

We explore the potential of CNN-based models for gallbladder cancer (GBC) detection from ultrasound (USG) images as no prior study is known. USG is the most common diagnostic modality for GB diseases due to its low cost and accessibility. However, USG images are challenging to analyze due to low image quality, noise, and varying viewpoints due to the handheld nature of the sensor. Our exhaustive study of state-of-the-art (soTA) image classification techniques for the problem reveals that they often fail to learn the salient GB region due to the presence of shadows in the USG images. S OTA object detection techniques also achieve low accuracy because of spurious textures due to noise or adjacent organs. We propose GBCNet to tackle the challenges in our problem. GBCNet first extracts the regions of interest (Ross) by detecting the GB (and not the cancer), and then uses a new multi-scale, second-order pooling architecture specializing in classifying GBC. To effectively handle spurious textures, we propose a curriculum inspired by human visual acuity, which reduces the texture biases in GBCNet. Experimental results demonstrate that GBCNet significantly outperforms SOTA CNN models, as well as the expert radiologists. Our technical innovations are generic to other USG image analysis tasks as well. Hence, as a validation, we also show the efficacy of GBCNet in detecting breast cancer from USG images. Project page with source code, trained models, and data is available at https://***/gbcnet.

关键词： visualization Computer vision Technological innovation Ultrasonic imaging Supervised learning Computer architecture Object detection

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