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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition"

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Leveraging Style and Content features for Text Conditioned Image Retrieval

Leveraging Style and Content features for Text Conditioned I...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Chawla, Pranit Jandial, Surgan Badjatiya, Pinkesh Chopra, Ayush Sarkar, Mausoom Krishnamurthy, Balaji IIT Kharagpur Kharagpur W Bengal India IIT Hyderabad Kandi Telangana India Adobe Media & Data Sci Res Lab San Jose CA USA MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665448994

Image Search is a fundamental task playing a significant role in the success of wide variety of frameworks and applications. However, with the increasing sizes of product catalogues and the number of attributes per product, it has become difficult for users to express their needs effectively. Therefore, we focus on the problem of Image Retrieval with Text Feedback, which involves retrieving modified images according to the natural language feedback provided by users. In this work, we hypothesise that since an image can be delineated by its content and style features, modifications to the image can also take place in the two sub spaces respectively. Hence, we decompose an input image into its corresponding style and content features, apply modification of the text feedback individually in both the style and content spaces and finally fuse them for retrieval. Our experiments show that our approach outperforms a recent state of the art method in this task, TIRG, that seeks to use a single vector in contrast to leveraging the modification via text over style and content spaces separately.

关键词： computer vision Fuses conferences Image retrieval Natural languages pattern recognition Task analysis

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Unsupervised Part Segmentation through Disentangling Appearance and Shape

Unsupervised Part Segmentation through Disentangling Appeara...

引用

ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Liu, Shilong Zhang, Lei Yang, Xiao Su, Hang Zhu, Jun Tsinghua Univ Dept Comp Sci & Tech BNRist Ctr Inst AITsinghua Bosch Joint ML Ctr Beijing 100084 Peoples R China Microsoft Corp Redmond WA 98052 USA

ISBN: (纸本)9781665445092

We study the problem of unsupervised discovery and segmentation of object parts, which, as an intermediate local representation, are capable of finding intrinsic object structure and providing more explainable recognition results. Recent unsupervised methods have greatly relaxed the dependency on annotated data which are costly to obtain, but still rely on additional information such as object segmentation mask or saliency map. To remove such a dependency and further improve the part segmentation performance, we develop a novel approach by disentangling the appearance and shape representations of object parts followed with reconstruction losses without using additional object mask information. To avoid degenerated solutions, a bottleneck block is designed to squeeze and expand the appearance representation, leading to a more effective disentanglement between geometry and appearance. Combined with a self-supervised part classification loss and an improved geometry concentration constraint, we can segment more consistent parts with semantic meanings. Comprehensive experiments on a wide variety of objects such as face, bird, and PASCAL VOC objects demonstrate the effectiveness of the proposed method.

关键词： Geometry computer vision Shape Annotations Face recognition Semantics Neural networks

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Linguistic Structures as Weak Supervision for Visual Scene Graph Generation

Linguistic Structures as Weak Supervision for Visual Scene G...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Ye, Keren Kovashka, Adriana Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15260 USA

ISBN: (纸本)9781665445092

Prior work in scene graph generation requires categorical supervision at the level of triplets-subjects and objects, and predicates that relate them, either with or without bounding box information. However, scene graph generation is a holistic task: thus holistic, contextual supervision should intuitively improve performance. In this work, we explore how linguistic structures in captions can benefit scene graph generation. Our method captures the information provided in captions about relations between individual triplets, and context for subjects and objects (e.g. visual properties are mentioned). Captions are a weaker type of supervision than triplets since the alignment between the exhaustive list of human-annotated subjects and objects in triplets, and the nouns in captions, is weak. However, given the large and diverse sources of multimodal data on the web (e.g. blog posts with images and captions), linguistic supervision is more scalable than crowdsourced triplets. We show extensive experimental comparisons against prior methods which leverage instance- and image-level supervision, and ablate our method to show the impact of leveraging phrasal and sequential context, and techniques to improve localization of subjects and objects.

关键词： Location awareness Visualization computer vision Blogs Linguistics pattern recognition Noise measurement

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DeepSurfels: Learning Online Appearance Fusion

DeepSurfels: Learning Online Appearance Fusion

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Mihajlovic, Marko Weder, Silvan Pollefeys, Marc Oswald, Martin R. Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland Microsoft Mixed Real Zurich Switzerland AI Zurich Lab Zurich Switzerland

ISBN: (纸本)9781665445092

We present DeepSurfels, a novel hybrid scene representation for geometry and appearance information. DeepSurfels combines explicit and neural building blocks to jointly encode geometry and appearance information. In contrast to established representations, DeepSurfels better represents high-frequency textures, is well-suited for online updates of appearance information, and can be easily combined with machine learning methods. We further present an end-to-end trainable online appearance fusion pipeline that fuses information from RGB images into the proposed scene representation and is trained using self-supervision imposed by the reprojection error with respect to the input images. Our method compares favorably to classical texture mapping approaches as well as recent learning-based techniques. Moreover, we demonstrate lower runtime, improved generalization capabilities, and better scalability to larger scenes compared to existing methods.

关键词： Geometry Runtime Shape Scalability Pipelines Machine learning Rendering (computer graphics)

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Firing pattern of Cells in the Hippocampus and Basolateral Amygdala during Novelty recognition 46

Firing Pattern of Cells in the Hippocampus and Basolateral A...

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46th Annual International conference of the ieee Engineering in Medicine and Biology Society, EMBC 2024

作者： Ren, Xiaoxin Wang, Yimeng Wang, Ling Yang, Jiajia Zheng, Chenguang Tianjin University Academy of Medical Engineering and Translational Medicine Medical College Tianjin China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin China

ISBN: (纸本)9798350371499

Novelty recognition is essential for exploring and collecting unknown but interesting information, in which multiple brain regions related to cognitive memory are involved. Dorsal hippocampus (dHPC) is revealed to be closely associated with spatial memory and basolateral amygdala (BLA) is reported recently to be related to state-dependent encoding of exploratory behavior. However, how the firing pattern of cells in these two brain regions responds to novelty dynamically remains unclear. In this study, we observed that the representations of cells in BLA exhibited shift towards novelty, whereas dHPC cells maintained strong stability after the introduction of novelty, which are both engaged in the execution of novel recognition. Our study may provide new evidence for underlying the internal mechanisms of perception of novelty in the brain. © 2024 ieee.

关键词： Brain

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Understanding the Robustness of 3D Object Detection with Bird'View Representations in Autonomous Driving

Understanding the Robustness of 3D Object Detection with Bir...

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2023 ieee/CVF conference on computer vision and pattern recognition, CVPR 2023

作者： Zhu, Zijian Zhang, Yichi Chen, Hai Dong, Yinpeng Zhao, Shu Ding, Wenbo Zhong, Jiachen Zheng, Shibao Institute of Image Communication and Network Engineering Shanghai Jiao Tong University China Institute for Ai Tsinghua University BNRist Center Thbi Lab Dept. of Comp. Sci. and Tech. China School of Computer Science and Technology Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Information Materials and Intelligent Sensing Laboratory of Anhui Province China Saic Motor Ai Lab Zhongguancun Laboratory China

ISBN: (纸本)9798350301298

3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the performance of 3D detectors with camera inputs on popular benchmarks. However, there still lacks a systematic understanding of the robustness of these vision-dependent BEV models, which is closely related to the safety of autonomous driving systems. In this paper, we evaluate the natural and adversarial robustness of various representative models under extensive settings, to fully understand their behaviors influenced by explicit BEV features compared with those without BEV. In addition to the classic settings, we propose a 3D consistent patch attack by applying adversarial patches in the 3D space to guarantee the spatiotemporal consistency, which is more realistic for the scenario of autonomous driving. With substantial experiments, we draw several findings: 1) BEV models tend to be more stable than previous methods under different natural conditions and common corruptions due to the expressive spatial representations;2) BEV models are more vulnerable to adversarial noises, mainly caused by the redundant BEV features;3) Camera-LiDARfusion models have superior performance under different settings with multi-modal inputs, but BEV fusion model is still vulnerable to adversarial noises of both point cloud and image. These findings alert the safety issue in the applications of BEV detectors and could facilitate the development of more robust models. © 2023 ieee.

关键词： Autonomous driving

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SkyStrokes: Bridging Artistic Expression and Communication Barriers Through Aerial Digital Canvas 6

SkyStrokes: Bridging Artistic Expression and Communication B...

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6th ieee International conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Bommala, Harikrishna Suvekha, Raman Reddy, Orra Venkata Srikanth Kumar, Neerati Shiva Rokati, Sai Kiran KG Reddy College of Engineering and Technology Department of Computer Science and Engineering Telanagana Moinabad India KG Reddy College of Engineering and Technology 4th Year Department of Computer Science and Engineering Hyderabad India

ISBN: (纸本)9798350306613

This endeavors to address pressing societal concerns by introducing innovative solutions. It confronts the challenges of communication barriers encountered by the hearing-impaired, the excessive reliance on smartphones, and the environmental impact of paper waste. Through the implementation of 'on-Air writing,' an application is proposed that tracks object movements, enabling users to draw on a computer screen by simply waving their hands. By merging technology and social responsibility, this initiative strives to empower the deaf and mute community while advocating for eco-conscious practices. In doing so, it not only revolutionizes communication but also encourages a more inclusive, tech-savvy, and environmentally aware world. © 2024 ieee.

关键词： computer vision Gesture recognition Hand tracking Human-computer Interaction Machine Learning Social Issues

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Compact and Effective Representations for Sketch-based Image Retrieval

Compact and Effective Representations for Sketch-based Image...

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ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Torres, Pablo Saavedra, Jose M. Univ Chile DCC Av Beauchef 851 Santiago Chile Impresee Inc 600 Calif St San Francisco CA USA

ISBN: (纸本)9781665448994

Sketch-based image retrieval (SBIR) has undergone an increasing interest in the community of computer vision bringing high impact in real applications. For instance, SBIR brings an increased benefit to eCommerce search engines because it allows users to formulate a query just by drawing what they need to buy. However, current methods showing high precision in retrieval work in a high dimensional space, which negatively affects aspects like memory consumption and time processing. Although some authors have also proposed compact representations, these drastically degrade the performance in a low dimension. Therefore in this work, we present different results of evaluating methods for producing compact embeddings in the context of sketch-based image retrieval. Our main interest is in strategies aiming to keep the local structure of the original space. The recent unsupervised local-topology preserving dimension reduction method UMAP fits our requirements and shows outstanding performance, improving even the precision achieved by SOTA methods. We evaluate six methods in two different datasets. We use Flickr15K and eCommerce datasets;the latter is another contribution of this work. We show that UMAP allows us to have feature vectors of 16 bytes improving precision by more than 35%.

关键词： Dimensionality reduction computer vision conferences Image retrieval Memory management Search engines pattern recognition

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Mongolian Compound Word Semantic Web Word Vector Evaluation Research 4

Mongolian Compound Word Semantic Web Word Vector Evaluation ...

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4th ieee International conference on pattern recognition and Machine Learning, PRML 2023

作者： La, Du Hasi Ding, Yang Inner Mongolia Normal University College of Computer Science and Technology Hohhot China College of Computer Science and Information Engineering Hohhot Minzu College Hohhot China

ISBN: (纸本)9798350324303

Semantic dictionaries are the basis of natural language processing tasks. With the construction of WordNet, CCD, HowNet and other semantic dictionaries, the semantic resources of Chinese and English have been greatly improved, and so has the construction of Mongolian lexical semantic network. As an important part of the lexical system, the construction project of Mongolian lexical semantic network is an essential step to fill in the perfect compound words. In this study, the Mongolian compound word semantic web based on the optimal word vector model is automatically generated by comparing the Word2Vec model, Glove model and FastText model for Mongolian word vectors in different dimensions and performing similarity and similarity on the test set. © 2023 ieee.

关键词： Semantic Web

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Extending 6D Object Pose Estimators for Stereo vision 4th

Extending 6D Object Pose Estimators for Stereo Vision

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4th International conference on pattern recognition and Artificial Intelligence, ICPRAI 2024

作者： Pöllabauer, Thomas Emrich, Jan Knauthe, Volker Kuijper, Arjan Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Fraunhoferstraße 5 Darmstadt Germany Technical University Darmstadt Karolinenplatz 5 Darmstadt Germany

ISBN: (纸本)9789819787043

Estimating the 6D pose of objects accurately, quickly, and robustly remains a difficult task. However, recent methods for directly regressing poses from RGB images using dense features have achieved state-of-the-art results. Stereo vision, which provides an additional perspective on the object, can help reduce pose ambiguity and occlusion. Moreover, stereo can directly infer the distance of an object, while mono-vision requires internalized knowledge of the object’s size. To extend the state-of-the-art in 6D object pose estimation to stereo, we created a BOP compatible stereo version of the YCB-V dataset called YCB-V DS and extended multiple state-of-the-art pose estimator algorithms to be applicable to stereo. Our method outperforms state-of-the-art 6D pose estimation algorithms by utilizing stereo vision and can easily be adopted for other dense feature-based algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stereo vision

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