Relevant bibliographies by topics / Visual and semantic embedding

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Visual and semantic embedding'

Author: Grafiati
Published: 25 May 2024
Last updated: 31 July 2025

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Journal articles on the topic "Visual and semantic embedding"

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Zhang, Yuanpeng, Jingye Guan, Haobo Wang, Kaiming Li, Ying Luo, and Qun Zhang. "Generalized Zero-Shot Space Target Recognition Based on Global-Local Visual Feature Embedding Network." Remote Sensing 15, no. 21 (2023): 5156. http://dx.doi.org/10.3390/rs15215156.

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Existing deep learning-based space target recognition methods rely on abundantly labeled samples and are not capable of recognizing samples from unseen classes without training. In this article, based on generalized zero-shot learning (GZSL), we propose a space target recognition framework to simultaneously recognize space targets from both seen and unseen classes. First, we defined semantic attributes to describe the characteristics of different categories of space targets. Second, we constructed a dual-branch neural network, termed the global-local visual feature embedding network (GLVFENet)
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Yeh, Mei-Chen, and Yi-Nan Li. "Multilabel Deep Visual-Semantic Embedding." IEEE Transactions on Pattern Analysis and Machine Intelligence 42, no. 6 (2020): 1530–36. http://dx.doi.org/10.1109/tpami.2019.2911065.

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Liu, Yang, Mengyuan Liu, Shudong Huang, and Jiancheng Lv. "Asymmetric Visual Semantic Embedding Framework for Efficient Vision-Language Alignment." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 5676–84. https://doi.org/10.1609/aaai.v39i6.32605.

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Learning visual semantic similarity is a critical challenge in bridging the gap between images and texts. However, there exist inherent variations between vision and language data, such as information density, i.e., images can contain textual information from multiple different views, which makes it difficult to compute the similarity between these two modalities accurately and efficiently. In this paper, we propose a novel framework called Asymmetric Visual Semantic Embedding (AVSE) to dynamically select features from various regions of images tailored to different textual inputs for similari
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Merkx, Danny, and Stefan L. Frank. "Learning semantic sentence representations from visually grounded language without lexical knowledge." Natural Language Engineering 25, no. 4 (2019): 451–66. http://dx.doi.org/10.1017/s1351324919000196.

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AbstractCurrent approaches to learning semantic representations of sentences often use prior word-level knowledge. The current study aims to leverage visual information in order to capture sentence level semantics without the need for word embeddings. We use a multimodal sentence encoder trained on a corpus of images with matching text captions to produce visually grounded sentence embeddings. Deep Neural Networks are trained to map the two modalities to a common embedding space such that for an image the corresponding caption can be retrieved and vice versa. We show that our model achieves re
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Ge, Jiannan, Hongtao Xie, Shaobo Min, and Yongdong Zhang. "Semantic-guided Reinforced Region Embedding for Generalized Zero-Shot Learning." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 2 (2021): 1406–14. http://dx.doi.org/10.1609/aaai.v35i2.16230.

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Generalized zero-shot Learning (GZSL) aims to recognize images from either seen or unseen domain, mainly by learning a joint embedding space to associate image features with the corresponding category descriptions. Recent methods have proved that localizing important object regions can effectively bridge the semantic-visual gap. However, these are all based on one-off visual localizers, lacking of interpretability and flexibility. In this paper, we propose a novel Semantic-guided Reinforced Region Embedding (SR2E) network that can localize important objects in the long-term interests to constr
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Zhou, Mo, Zhenxing Niu, Le Wang, Zhanning Gao, Qilin Zhang, and Gang Hua. "Ladder Loss for Coherent Visual-Semantic Embedding." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 13050–57. http://dx.doi.org/10.1609/aaai.v34i07.7006.

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For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way – relevant or irrelevant, and all “irrelevant” candidates are uniformly pushed away from the query by an equal margin in the embedding space, regardless of their various proximity to the query. This practice disregards relatively discriminative information and could lead to suboptimal ranking in the retrieval results and poorer user experience, especially in the long-tail query scenario where a matching candidate may not necessarily exist. In this paper, we introduce
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Nguyen, Huy Manh, Tomo Miyazaki, Yoshihiro Sugaya, and Shinichiro Omachi. "Multiple Visual-Semantic Embedding for Video Retrieval from Query Sentence." Applied Sciences 11, no. 7 (2021): 3214. http://dx.doi.org/10.3390/app11073214.

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Visual-semantic embedding aims to learn a joint embedding space where related video and sentence instances are located close to each other. Most existing methods put instances in a single embedding space. However, they struggle to embed instances due to the difficulty of matching visual dynamics in videos to textual features in sentences. A single space is not enough to accommodate various videos and sentences. In this paper, we propose a novel framework that maps instances into multiple individual embedding spaces so that we can capture multiple relationships between instances, leading to com
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MATSUBARA, Takashi. "Target-Oriented Deformation of Visual-Semantic Embedding Space." IEICE Transactions on Information and Systems E104.D, no. 1 (2021): 24–33. http://dx.doi.org/10.1587/transinf.2020mup0003.

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Keller, Patrick, Abdoul Kader Kaboré, Laura Plein, Jacques Klein, Yves Le Traon, and Tegawendé F. Bissyandé. "What You See is What it Means! Semantic Representation Learning of Code based on Visualization and Transfer Learning." ACM Transactions on Software Engineering and Methodology 31, no. 2 (2022): 1–34. http://dx.doi.org/10.1145/3485135.

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Recent successes in training word embeddings for Natural Language Processing ( NLP ) tasks have encouraged a wave of research on representation learning for source code, which builds on similar NLP methods. The overall objective is then to produce code embeddings that capture the maximum of program semantics. State-of-the-art approaches invariably rely on a syntactic representation (i.e., raw lexical tokens, abstract syntax trees, or intermediate representation tokens) to generate embeddings, which are criticized in the literature as non-robust or non-generalizable. In this work, we investigat
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Tang, Qi, Yao Zhao, Meiqin Liu, Jian Jin, and Chao Yao. "Semantic Lens: Instance-Centric Semantic Alignment for Video Super-resolution." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (2024): 5154–61. http://dx.doi.org/10.1609/aaai.v38i6.28321.

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As a critical clue of video super-resolution (VSR), inter-frame alignment significantly impacts overall performance. However, accurate pixel-level alignment is a challenging task due to the intricate motion interweaving in the video. In response to this issue, we introduce a novel paradigm for VSR named Semantic Lens, predicated on semantic priors drawn from degraded videos. Specifically, video is modeled as instances, events, and scenes via a Semantic Extractor. Those semantics assist the Pixel Enhancer in understanding the recovered contents and generating more realistic visual results. The
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Dissertations / Theses on the topic "Visual and semantic embedding"

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Engilberge, Martin. "Deep Inside Visual-Semantic Embeddings." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS150.

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De nos jours l’Intelligence artificielle (IA) est omniprésente dans notre société. Le récent développement des méthodes d’apprentissage basé sur les réseaux de neurones profonds aussi appelé “Deep Learning” a permis une nette amélioration des modèles de représentation visuelle et textuelle. Cette thèse aborde la question de l’apprentissage de plongements multimodaux pour représenter conjointement des données visuelles et sémantiques. C’est une problématique centrale dans le contexte actuel de l’IA et du deep learning, qui présente notamment un très fort potentiel pour l’interprétabilité des mo
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Wang, Qian. "Zero-shot visual recognition via latent embedding learning." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/zeroshot-visual-recognition-via-latent-embedding-learning(bec510af-6a53-4114-9407-75212e1a08e1).html.

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Traditional supervised visual recognition methods require a great number of annotated examples for each concerned class. The collection and annotation of visual data (e.g., images and videos) could be laborious, tedious and time-consuming when the number of classes involved is very large. In addition, there are such situations where the test instances are from novel classes for which training examples are unavailable in the training stage. These issues can be addressed by zero-shot learning (ZSL), an emerging machine learning technique enabling the recognition of novel classes. The key issue i
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Ficapal, Vila Joan. "Anemone: a Visual Semantic Graph." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-252810.

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Semantic graphs have been used for optimizing various natural language processing tasks as well as augmenting search and information retrieval tasks. In most cases these semantic graphs have been constructed through supervised machine learning methodologies that depend on manually curated ontologies such as Wikipedia or similar. In this thesis, which consists of two parts, we explore in the first part the possibility to automatically populate a semantic graph from an ad hoc data set of 50 000 newspaper articles in a completely unsupervised manner. The utility of the visual representation of th
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Jakeš, Jan. "Visipedia - Embedding-driven Visual Feature Extraction and Learning." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236120.

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Multidimenzionální indexování je účinným nástrojem pro zachycení podobností mezi objekty bez nutnosti jejich explicitní kategorizace. V posledních letech byla tato metoda hojně využívána pro anotaci objektů a tvořila významnou část publikací spojených s projektem Visipedia. Tato práce analyzuje možnosti strojového učení z multidimenzionálně indexovaných obrázků na základě jejich obrazových příznaků a přestavuje metody predikce multidimenzionálních souřadnic pro předem neznámé obrázky. Práce studuje příslušené algoritmy pro extrakci příznaků, analyzuje relevantní metody strojového účení a popis
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Gao, Jizhou. "VISUAL SEMANTIC SEGMENTATION AND ITS APPLICATIONS." UKnowledge, 2013. http://uknowledge.uky.edu/cs_etds/14.

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This dissertation addresses the difficulties of semantic segmentation when dealing with an extensive collection of images and 3D point clouds. Due to the ubiquity of digital cameras that help capture the world around us, as well as the advanced scanning techniques that are able to record 3D replicas of real cities, the sheer amount of visual data available presents many opportunities for both academic research and industrial applications. But the mere quantity of data also poses a tremendous challenge. In particular, the problem of distilling useful information from such a large repository of
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Liu, Jingen. "Learning Semantic Features for Visual Recognition." Doctoral diss., University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3358.

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Visual recognition (e.g., object, scene and action recognition) is an active area of research in computer vision due to its increasing number of real-world applications such as video (image) indexing and search, intelligent surveillance, human-machine interaction, robot navigation, etc. Effective modeling of the objects, scenes and actions is critical for visual recognition. Recently, bag of visual words (BoVW) representation, in which the image patches or video cuboids are quantized into visual words (i.e., mid-level features) based on their appearance similarity using clustering, has been wi
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Nguyen, Duc Minh Chau. "Affordance learning for visual-semantic perception." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2021. https://ro.ecu.edu.au/theses/2443.

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Affordance Learning is linked to the study of interactions between robots and objects, including how robots perceive objects by scene understanding. This area has been popular in the Psychology, which has recently come to influence Computer Vision. In this way, Computer Vision has borrowed the concept of affordance from Psychology in order to develop Visual-Semantic recognition systems, and to develop the capabilities of robots to interact with objects, in particular. However, existing systems of Affordance Learning are still limited to detecting and segmenting object affordances, which is cal
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Chen, Yifu. "Deep learning for visual semantic segmentation." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS200.

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Dans cette thèse, nous nous intéressons à la segmentation sémantique visuelle, une des tâches de haut niveau qui ouvre la voie à une compréhension complète des scènes. Plus précisément, elle requiert une compréhension sémantique au niveau du pixel. Avec le succès de l’apprentissage approfondi de ces dernières années, les problèmes de segmentation sémantique sont abordés en utilisant des architectures profondes. Dans la première partie, nous nous concentrons sur la construction d’une fonction de coût plus appropriée pour la segmentation sémantique. En particulier, nous définissons une nouvelle
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Fan, Wei. "Image super-resolution using neighbor embedding over visual primitive manifolds /." View abstract or full-text, 2007. http://library.ust.hk/cgi/db/thesis.pl?CSED%202007%20FAN.

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Hanwell, David. "Weakly supervised learning of visual semantic attributes." Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.687063.

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There are at present many billions of images on the internet, only a fraction of which are labelled according to their semantic content. To automatically provide labels for the rest, models of visual semantic concepts must be created. Such models are traditionally trained using images which have been manually acquired, segmented, and labelled. In this thesis, we submit that such models can be learned automatically using those few images which have already been labelled, either directly by their creators, or indirectly by their associated text. Such imagery can be acquired easily, cheaply, and
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Books on the topic "Visual and semantic embedding"

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Endert, Alex. Semantic Interaction for Visual Analytics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-031-02603-4.

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Fieled, Adam, ed. The Semantic Limitations of Visual Poetry. P.F.S. Post, 2009.

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Paquette, Gilbert. Visual knowledge modeling for semantic web technologies: Models and ontologies. Information Science Reference, 2010.

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Hussam, Ali. Semantic highlighting: An approach to communicating information and knowledge through visual metadata. The Author], 1999.

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Valkola, Jarmo. Perceiving the visual in cinema: Semantic approaches to film form and meaning. Jyväskylän Yliopisto, 1993.

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Chen, Chaomei. Effects of spatial-semantic interfaces in visual information retrieval: Three experimental studies. Resource, 2002.

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K, kokula Krishna Hari, ed. Multi-secret Semantic Visual Cryptographic Protocol for Securing Image Communications: ICCS 2014. Association of Scientists, Developers and Faculties, 2014.

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Bratko, Aleksandr. Artificial intelligence, legal system and state functions. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064996.

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The monograph deals with methodological problems of embedding artificial intelligence in the legal system taking into account the laws of society. Describes the properties of the rule of law as a Microsystem in subsystems of law and methods of its fixation in the system of law and logic of legal norms. Is proposed and substantiated the idea of creating specifically for artificial intelligence, separate and distinct, unambiguous normative system, parallel to the principal branches of law is built on the logic of the four-membered structure of legal norms. Briefly discusses some of the theory of
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Stoenescu, Livia. The Pictorial Art of El Greco. Amsterdam University Press, 2019. http://dx.doi.org/10.5117/9789462989009.

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The Pictorial Art of El Greco: Transmaterialities, Temporalities, and Media investigates El Greco’s pictorial art as foundational to the globalising trends manifested in the visual culture of early modernity. It also exposes the figurative, semantic, and allegorical senses that El Greco created to challenge an Italian Renaissance-centered discourse. Even though he was guided by the unprecedented burgeoning of devotional art in the post-Tridentine decades and by the expressive possibilities of earlier religious artifacts, especially those inherited from the apostolic past, the author demonstrat
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Zhang, Yu-jin. Semantic-Based Visual Information Retrieval. IRM Press, 2006.

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Book chapters on the topic "Visual and semantic embedding"

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Wang, Haoran, Ying Zhang, Zhong Ji, Yanwei Pang, and Lin Ma. "Consensus-Aware Visual-Semantic Embedding for Image-Text Matching." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58586-0_2.

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Yang, Zhanbo, Li Li, Jun He, Zixi Wei, Li Liu, and Jun Liao. "Multimodal Learning with Triplet Ranking Loss for Visual Semantic Embedding Learning." In Knowledge Science, Engineering and Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29551-6_67.

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Jiang, Zhukai, and Zhichao Lian. "Self-supervised Visual-Semantic Embedding Network Based on Local Label Optimization." In Machine Learning for Cyber Security. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20102-8_31.

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Filntisis, Panagiotis Paraskevas, Niki Efthymiou, Gerasimos Potamianos, and Petros Maragos. "Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss." In Computer Vision – ECCV 2020 Workshops. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66415-2_52.

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Syed, Arsal, and Brendan Tran Morris. "CNN, Segmentation or Semantic Embeddings: Evaluating Scene Context for Trajectory Prediction." In Advances in Visual Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64559-5_56.

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Valério, Rodrigo, and João Magalhães. "Learning Semantic-Visual Embeddings with a Priority Queue." In Pattern Recognition and Image Analysis. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-36616-1_6.

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Schall, Konstantin, Nico Hezel, Klaus Jung, and Kai Uwe Barthel. "Vibro: Video Browsing with Semantic and Visual Image Embeddings." In MultiMedia Modeling. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-27077-2_56.

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Yang, Bo, and Shanping Li. "GUI2DSVec: Detecting Visual Design Smells Based on Semantic Embedding of GUI Images and Components." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63992-0_29.

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Chen, Yanbei, and Loris Bazzani. "Learning Joint Visual Semantic Matching Embeddings for Language-Guided Retrieval." In Computer Vision – ECCV 2020. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58542-6_9.

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Theodoridou, Christina, Andreas Kargakos, Ioannis Kostavelis, Dimitrios Giakoumis, and Dimitrios Tzovaras. "Spatially-Constrained Semantic Segmentation with Topological Maps and Visual Embeddings." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87156-7_10.

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Conference papers on the topic "Visual and semantic embedding"

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Ning, Baiyang, Yan Huang, and Liang Wang. "GVSE++: improved gated visual-semantic embedding for few-shot image and sentence matching." In Seventeenth International Conference on Digital Image Processing (ICDIP 2025), edited by Xudong Jiang, Jindong Tian, Ting-Chung Poon, and Zhaohui Wang. SPIE, 2025. https://doi.org/10.1117/12.3073443.

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Hao, Dongze, Qunbo Wang, and Jing Liu. "Semantic-Visual Graph Reasoning for Visual Dialog." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687523.

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Li, Zheng, Caili Guo, Zerun Feng, Jenq-Neng Hwang, and Xijun Xue. "Multi-View Visual Semantic Embedding." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/158.

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Visual Semantic Embedding (VSE) is a dominant method for cross-modal vision-language retrieval. Its purpose is to learn an embedding space so that visual data can be embedded in a position close to the corresponding text description. However, there are large intra-class variations in the vision-language data. For example, multiple texts describing the same image may be described from different views, and the descriptions of different views are often dissimilar. The mainstream VSE method embeds samples from the same class in similar positions, which will suppress intra-class variations and lead
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Ren, Zhou, Hailin Jin, Zhe Lin, Chen Fang, and Alan Yuille. "Multiple Instance Visual-Semantic Embedding." In British Machine Vision Conference 2017. British Machine Vision Association, 2017. http://dx.doi.org/10.5244/c.31.89.

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Wehrmann, Jônatas, and Rodrigo C. Barros. "Language-Agnostic Visual-Semantic Embeddings." In Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/ctd.2021.15751.

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We propose a framework for training language-invariant cross-modal retrieval models. We introduce four novel text encoding approaches, as well as a character-based word-embedding approach, allowing the model to project similar words across languages into the same word-embedding space. In addition, by performing cross-modal retrieval at the character level, the storage requirements for a text encoder decrease substantially, allowing for lighter and more scalable retrieval architectures. The proposed language-invariant textual encoder based on characters is virtually unaffected in terms of stora
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Li, Binglin, and Yang Wang. "Visual Relationship Detection Using Joint Visual-Semantic Embedding." In 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. http://dx.doi.org/10.1109/icpr.2018.8546097.

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Ji, Rongrong, Hongxun Yao, Xiaoshuai Sun, Bineng Zhong, and Wen Gao. "Towards semantic embedding in visual vocabulary." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5540118.

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Hong, Ziming, Shiming Chen, Guo-Sen Xie, et al. "Semantic Compression Embedding for Generative Zero-Shot Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/134.

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Generative methods have been successfully applied in zero-shot learning (ZSL) by learning an implicit mapping to alleviate the visual-semantic domain gaps and synthesizing unseen samples to handle the data imbalance between seen and unseen classes. However, existing generative methods simply use visual features extracted by the pre-trained CNN backbone. These visual features lack attribute-level semantic information. Consequently, seen classes are indistinguishable, and the knowledge transfer from seen to unseen classes is limited. To tackle this issue, we propose a novel Semantic Compression
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Perez-Martin, Jesus, Jorge Perez, and Benjamin Bustos. "Visual-Syntactic Embedding for Video Captioning." In LatinX in AI at Computer Vision and Pattern Recognition Conference 2021. Journal of LatinX in AI Research, 2021. http://dx.doi.org/10.52591/lxai202106259.

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Video captioning is the task of predicting a semantic and syntactically correct sequence of words given some context video. The most successful methods for video captioning have a strong dependency on the effectiveness of semantic representations learned from visual models, but often produce syntactically incorrect sentences which harms their performance on standard datasets. We address this limitation by considering syntactic representation learning as an essential component of video captioning. We construct a visual-syntactic embedding by mapping into a common vector space a visual represent
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Zeng, Zhixian, Jianjun Cao, Nianfeng Weng, Guoquan Jiang, Yizhuo Rao, and Yuxin Xu. "Softmax Pooling for Super Visual Semantic Embedding." In 2021 IEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON). IEEE, 2021. http://dx.doi.org/10.1109/iemcon53756.2021.9623131.

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Reports on the topic "Visual and semantic embedding"

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Kud, A. A. Figures and Tables. Reprinted from “Comprehensive сlassification of virtual assets”, A. A. Kud, 2021, International Journal of Education and Science, 4(1), 52–75. KRPOCH, 2021. http://dx.doi.org/10.26697/reprint.ijes.2021.1.6.a.kud.

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Figure. Distributed Ledger Token Accounting System. Figure. Subjects of Social Relations Based on the Decentralized Information Platform. Figure. Derivativeness of a Digital Asset. Figure. Semantic Features of the Concept of a “Digital Asset” in Economic and Legal Aspects. Figure. Derivativeness of Polyassets and Monoassets. Figure. Types of Tokenized Assets Derived from Property. Figure. Visual Representation of the Methods of Financial and Management Accounting of Property Using Various Types of Tokenized Assets. Figure. Visual Representation of the Classification of Virtual Assets Based on
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Tabinskyy, Yaroslav. VISUAL CONCEPTS OF PHOTO IN THE MEDIA (ON THE EXAMPLE OF «UKRAINER» AND «REPORTERS»). Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.50.11099.

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The article is devoted to the analysis of the main forms of visualization in the media related to photo. The thematic visual concepts are described in accordance with the content of electronic media, which consider the impact of modern technologies on the development of media space. The researches of the Ukrainian and foreign educational institutions concerning the main features of modern photo is classificate. Modifications and new visual forms in the media are singled out. The main objective of the article is to study the visual concepts of modern photo and identify ideological and thematic
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Mbani, Benson, Timm Schoening, and Jens Greinert. Automated and Integrated Seafloor Classification Workflow (AI-SCW). GEOMAR, 2023. http://dx.doi.org/10.3289/sw_2_2023.

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The Automated and Integrated Seafloor Classification Workflow (AI-SCW) is a semi-automated underwater image processing pipeline that has been customized for use in classifying the seafloor into semantic habitat categories. The current implementation has been tested against a sequence of underwater images collected by the Ocean Floor Observation System (OFOS), in the Clarion-Clipperton Zone of the Pacific Ocean. Despite this, the workflow could also be applied to images acquired by other platforms such as an Autonomous Underwater Vehicle (AUV), or Remotely Operated Vehicle (ROV). The modules in
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Yatsymirska, Mariya. SOCIAL EXPRESSION IN MULTIMEDIA TEXTS. Ivan Franko National University of Lviv, 2021. http://dx.doi.org/10.30970/vjo.2021.49.11072.

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The article investigates functional techniques of extralinguistic expression in multimedia texts; the effectiveness of figurative expressions as a reaction to modern events in Ukraine and their influence on the formation of public opinion is shown. Publications of journalists, broadcasts of media resonators, experts, public figures, politicians, readers are analyzed. The language of the media plays a key role in shaping the worldview of the young political elite in the first place. The essence of each statement is a focused thought that reacts to events in the world or in one’s own country. Th
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