Relevant bibliographies by topics / Cross-modality Translation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Cross-modality Translation'

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Published: 8 March 2025

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Journal articles on the topic "Cross-modality Translation"

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Holubenko, Nataliia. "Modality from the Cross-cultural Studies Perspective: a Practical Approach to Intersemiotic Translation." World Journal of English Language 13, no. 2 (January 27, 2023): 86. http://dx.doi.org/10.5430/wjel.v13n2p86.

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Any scientific question should be understood as a process of dynamic semiosis in search of truth. The revelatory web is goal-oriented (teleological), but with no stable outcome, static method, redefinition, or fixed agent. All outcomes, methods, and agents are temporary and are temporary “trends” in translation studies that can be abandoned for new ones. The translation can be categorized as a fragmented record or metaphorically as a mosaic, whose components allow the construction of a figurative, diegetic, dramatic world in intersemiotic translation, to be inscribed in the diagram of the narrative. The translation adopts the repetitive and non-repeating behavior patterns of a particular culture, rejecting trendy or outdated translation tools. The same applies to intersemiotic translation with interpretive and reinterpretive meaning. The ideas of the classics about a global approach to semiolinguistics have turned the whole traditional approach to translation studies upside down. The traditional view of the question of intercultural, intersemiotic translation focused on untested dichotomies labeled as dogmatic forms of double self-reflection. Intersemiotic translation offers experimental and temporal responses of a skeptical and evolutionary nature at the boundaries of the translated and untranslatable, correspondence and non-correspondence, conformity and unconformity, the starring role and purpose of intelligence, the dynamism and emotionality of the Falabilist spirit and the Falabilist heart of the translator. It focuses on the concepts of translation and retranslation, the fate of the intercultural text, the fate of the target text, and other semiotic issues of translation in the broadest sense, in the sense of an encoded phenomenon rather than an intersemiotic code. This paper analyzes cultural and linguistic transsemiosis from the perspective of translation and transduction to reveal the essence of intersemiosis. One considers the extrapolarity and complexity phenomenon of modality in terms of cognitive-discursive and semiotic features of its manifestation during translation. In the contemporary scientific pattern, the linguistic category of modality is considered as a functional-semantic, semantic-pragmatic, semantic- syntactic, syntactic, grammatical or logical category. One defines it as the inner attitude of the narrator to the content. The essence of modality in intersemiotic translatin is related to the inner linguistic thinking. Accordingly, intersemiotic translation is the recoding of the original text by means of another sign (semiotic) system.
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Liu, Ajian, Zichang Tan, Jun Wan, Yanyan Liang, Zhen Lei, Guodong Guo, and Stan Z. Li. "Face Anti-Spoofing via Adversarial Cross-Modality Translation." IEEE Transactions on Information Forensics and Security 16 (2021): 2759–72. http://dx.doi.org/10.1109/tifs.2021.3065495.

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Rabadán, Rosa. "Modality and modal verbs in contrast." Languages in Contrast 6, no. 2 (December 15, 2006): 261–306. http://dx.doi.org/10.1075/lic.6.2.04rab.

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This paper addresses the question of how English and Spanish encode the modal meanings of possibility and necessity. English modals and Spanish modal periphrases emerge as ‘cross-linguistic equivalents’ in this area. Data from two monolingual ‘comparable’ corpora — the Bank of English and CREA — reveal (i) differences in grammatical conceptualization in the English and the Spanish traditions and (ii) the relative inadequacy of classifications of modality for a translation-oriented contrast in this area. An English-Spanish contrastive map of the semantics (and expressive means) of modality will be an effective way to make relevant and accurate cross-linguistic information available. It is also the first step towards identifying potential translation pitfalls.
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Wang, Yu, and Jianping Zhang. "CMMCSegNet: Cross-Modality Multicascade Indirect LGE Segmentation on Multimodal Cardiac MR." Computational and Mathematical Methods in Medicine 2021 (June 5, 2021): 1–14. http://dx.doi.org/10.1155/2021/9942149.

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Since Late-Gadolinium Enhancement (LGE) of cardiac magnetic resonance (CMR) visualizes myocardial infarction, and the balanced-Steady State Free Precession (bSSFP) cine sequence can capture cardiac motions and present clear boundaries; multimodal CMR segmentation has played an important role in the assessment of myocardial viability and clinical diagnosis, while automatic and accurate CMR segmentation still remains challenging due to a very small amount of labeled LGE data and the relatively low contrasts of LGE. The main purpose of our work is to learn the real/fake bSSFP modality with ground truths to indirectly segment the LGE modality of cardiac MR by using a proposed cross-modality multicascade framework: cross-modality translation network and automatic segmentation network, respectively. In the segmentation stage, a novel multicascade pix2pix network is designed to segment the fake bSSFP sequence obtained from a cross-modality translation network. Moreover, we propose perceptual loss measuring features between ground truth and prediction, which are extracted from the pretrained vgg network in the segmentation stage. We evaluate the performance of the proposed method on the multimodal CMR dataset and verify its superiority over other state-of-the-art approaches under different network structures and different types of adversarial losses in terms of dice accuracy in testing. Therefore, the proposed network is promising for Indirect Cardiac LGE Segmentation in clinical applications.
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Danni, Yu. "A Genre Approach to the Translation of Political Speeches Based on a Chinese-Italian-English Trilingual Parallel Corpus." SAGE Open 10, no. 2 (April 2020): 215824402093360. http://dx.doi.org/10.1177/2158244020933607.

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Using a trilingual parallel corpus, this article investigates the translation of Chinese political speeches in Italian and English, with the aim to explore cross-linguistic variations regarding translation shifts of key functional elements in the genre of political speeches. The genre-based methodology includes a rhetorical move analysis, which is used to highlight key functional elements of the genre, and a functional grammar analysis of translation shifts of the lexico-grammatical elements identified in the previous stage. The findings show that the core communicative function of the genre is “Proposing deontic statements,” and modality of obligation is essential for the realization of this rhetorical function. Afterwards, the analysis of translation shifts of deontic modality reveals that the English translation is characterized by higher modality value shifts in comparison to the Italian translation. This difference may be related to the degree of autonomy in translation choice and understanding of the communicative purposes of the translation genre. In terms of methodological implications, this functionalist approach attempts to providing insights into the communicative purposes of the translation genre by focusing on how key functional elements are translated.
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Wu, Kevin E., Kathryn E. Yost, Howard Y. Chang, and James Zou. "BABEL enables cross-modality translation between multiomic profiles at single-cell resolution." Proceedings of the National Academy of Sciences 118, no. 15 (April 7, 2021): e2023070118. http://dx.doi.org/10.1073/pnas.2023070118.

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Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility—for example, generating paired measurements of single-cell transcriptome (single-cell RNA sequencing [scRNA-seq]) and chromatin accessibility (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq])—widespread application of joint profiling is challenging due to its experimental complexity, noise, and cost. Here, we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging an interoperable neural network model, BABEL can predict single-cell expression directly from a cell’s scATAC-seq and vice versa after training on relevant data. This makes it possible to computationally synthesize paired multiomic measurements when only one modality is experimentally available. Across several paired single-cell ATAC and gene expression datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to cell types within new biological contexts not seen during training. Starting from scATAC-seq of patient-derived basal cell carcinoma (BCC), BABEL generated single-cell expression that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of experimental BCC scRNA-seq data for diverse cell types related to BABEL’s training data. We further show that BABEL can incorporate additional single-cell data modalities, such as protein epitope profiling, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation.
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Sharma, Akanksha, and Neeru Jindal. "Cross-Modality Breast Image Translation with Improved Resolution Using Generative Adversarial Networks." Wireless Personal Communications 119, no. 4 (March 29, 2021): 2877–91. http://dx.doi.org/10.1007/s11277-021-08376-5.

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Mai, Sijie, Haifeng Hu, and Songlong Xing. "Modality to Modality Translation: An Adversarial Representation Learning and Graph Fusion Network for Multimodal Fusion." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 164–72. http://dx.doi.org/10.1609/aaai.v34i01.5347.

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Learning joint embedding space for various modalities is of vital importance for multimodal fusion. Mainstream modality fusion approaches fail to achieve this goal, leaving a modality gap which heavily affects cross-modal fusion. In this paper, we propose a novel adversarial encoder-decoder-classifier framework to learn a modality-invariant embedding space. Since the distributions of various modalities vary in nature, to reduce the modality gap, we translate the distributions of source modalities into that of target modality via their respective encoders using adversarial training. Furthermore, we exert additional constraints on embedding space by introducing reconstruction loss and classification loss. Then we fuse the encoded representations using hierarchical graph neural network which explicitly explores unimodal, bimodal and trimodal interactions in multi-stage. Our method achieves state-of-the-art performance on multiple datasets. Visualization of the learned embeddings suggests that the joint embedding space learned by our method is discriminative.
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Lee, Yong-Hyeok, Dong-Won Jang, Jae-Bin Kim, Rae-Hong Park, and Hyung-Min Park. "Audio–Visual Speech Recognition Based on Dual Cross-Modality Attentions with the Transformer Model." Applied Sciences 10, no. 20 (October 17, 2020): 7263. http://dx.doi.org/10.3390/app10207263.

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Since attention mechanism was introduced in neural machine translation, attention has been combined with the long short-term memory (LSTM) or replaced the LSTM in a transformer model to overcome the sequence-to-sequence (seq2seq) problems with the LSTM. In contrast to the neural machine translation, audio–visual speech recognition (AVSR) may provide improved performance by learning the correlation between audio and visual modalities. As a result that the audio has richer information than the video related to lips, AVSR is hard to train attentions with balanced modalities. In order to increase the role of visual modality to a level of audio modality by fully exploiting input information in learning attentions, we propose a dual cross-modality (DCM) attention scheme that utilizes both an audio context vector using video query and a video context vector using audio query. Furthermore, we introduce a connectionist-temporal-classification (CTC) loss in combination with our attention-based model to force monotonic alignments required in AVSR. Recognition experiments on LRS2-BBC and LRS3-TED datasets showed that the proposed model with the DCM attention scheme and the hybrid CTC/attention architecture achieved at least a relative improvement of 7.3% on average in the word error rate (WER) compared to competing methods based on the transformer model.
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Wang, Yabing, Fan Wang, Jianfeng Dong, and Hao Luo. "CL2CM: Improving Cross-Lingual Cross-Modal Retrieval via Cross-Lingual Knowledge Transfer." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 6 (March 24, 2024): 5651–59. http://dx.doi.org/10.1609/aaai.v38i6.28376.

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Cross-lingual cross-modal retrieval has garnered increasing attention recently, which aims to achieve the alignment between vision and target language (V-T) without using any annotated V-T data pairs. Current methods employ machine translation (MT) to construct pseudo-parallel data pairs, which are then used to learn a multi-lingual and multi-modal embedding space that aligns visual and target-language representations. However, the large heterogeneous gap between vision and text, along with the noise present in target language translations, poses significant challenges in effectively aligning their representations. To address these challenges, we propose a general framework, Cross-Lingual to Cross-Modal (CL2CM), which improves the alignment between vision and target language using cross-lingual transfer. This approach allows us to fully leverage the merits of multi-lingual pre-trained models (e.g., mBERT) and the benefits of the same modality structure, i.e., smaller gap, to provide reliable and comprehensive semantic correspondence (knowledge) for the cross-modal network. We evaluate our proposed approach on two multilingual image-text datasets, Multi30K and MSCOCO, and one video-text dataset, VATEX. The results clearly demonstrate the effectiveness of our proposed method and its high potential for large-scale retrieval.
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Dissertations / Theses on the topic "Cross-modality Translation"

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Longuefosse, Arthur. "Apprentissage profond pour la conversion d’IRM vers TDM en imagerie thoracique." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0489.

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L’imagerie thoracique présente des défis importants, chaque modalité d’imagerie ayant ses propres limitations. Le scanner (CT), référence en imagerie pulmonaire, offre une haute résolution spatiale, mais utilise des rayonnements ionisants, posant des risques pour les patients nécessitant des examens fréquents. À l’inverse, l’IRM pulmonaire offre une alternative sans radiation, mais est limitée par des problèmes techniques tels qu’un faible contraste et des artefacts, limitant son adoption clinique à grande échelle. Récemment, l’IRM à temps d’écho ultracourt (UTE-MRI) a montré un potentiel pour surmonter certaines de ces limitations, mais elle ne parvient toujours pas à atteindre la haute résolution et la qualité d’image du scanner, notamment pour l’évaluation détaillée des structures. L’objectif principal de cette thèse est de développer et valider des modèles basés sur l’apprentissage profond pour synthétiser des images de scanner TDM à partir d’IRM UTE. Plus précisément, nous visons à évaluer la qualité des images, la reconstruction anatomique et l’applicabilité clinique de ces images de synthèse par rapport aux IRM UTE originales et aux scanners CT correspondants. Dans un premier temps, nous avons exploré les bases de la synthèse d’images médicales, en établissant le socle pour la conversion de l’IRM vers le scanner. Nous avons mis en œuvre un modèle de GAN 2D basé sur le cadre pix2pixHD, optimisé avec la normalisation SPADE et affiné les techniques de prétraitement telles que le rééchantillonnage et l’enregistrement. L’évaluation clinique par des radiologues experts a montré des résultats prometteurs en comparant les images synthétiques aux scans réels. La synthèse a été ensuite améliorée par l’introduction de la perte perceptuelle, qui a affiné les détails structurels et la qualité visuelle, et par l’intégration de stratégies 2.5D pour équilibrer la synthèse 2D et 3D. De plus, nous avons mis l’accent sur un processus de validation rigoureux utilisant des métriques spécifiques à la tâche, remettant en question les métriques globales traditionnelles basées sur l’intensité, en nous concentrant sur la reconstruction précise des structures anatomiques. Dans la dernière étape, nous avons développé un cadre de synthèse 3D robuste et évolutif en adaptant nnU-Net pour la génération de scanner, accompagné d’une fonction de perte priorisant les caractéristiques anatomiques, permettant une meilleure reconstruction des structures critiques telles que les voies respiratoires et les vaisseaux. Notre travail met en évidence le potentiel des modèles basés sur l’apprentissage profond pour générer des images synthétiques de haute qualité de type scanner à partir d’IRM UTE, offrant une amélioration significative de l’imagerie pulmonaire non invasive. Ces avancées pourraient considérablement améliorer l’applicabilité clinique de l’IRM UTE, en offrant une alternative plus sûre au scanner pour le suivi des maladies pulmonaires chroniques. De plus, un brevet est actuellement en préparation pour l’adoption de notre méthode, ouvrant la voie à une utilisation potentielle en milieu clinique
Thoracic imaging faces significant challenges, with each imaging modality presenting its own limitations. CT, the gold standard for lung imaging, delivers high spatial resolution but relies on ionizing radiation, posing risks for patients requiring frequent scans. Conversely, lung MRI, offers a radiation-free alternative but is hindered by technical issues such as low contrast and artifacts, limiting its broader clinical use. Recently, UTE-MRI shows promise in addressing some of these limitations, but still lacks the high resolution and image quality of CT, particularly for detailed structural assessment. The primary objective of this thesis is to develop and validate deep learning-based models for synthesizing CT-like images from UTE-MRI. Specifically, we aim to assess the image quality, anatomical accuracy, and clinical applicability of these synthetic CT images in comparison to the original UTE-MRI and real CT scans in thoracic imaging. Initially, we explored the fundamentals of medical image synthesis, establishing the groundwork for MR to CT translation. We implemented a 2D GAN model based on the pix2pixHD framework, optimizing it using SPADE normalization and refining preprocessing techniques such as resampling and registration. Clinical evaluation with expert radiologists showed promising results in comparing synthetic images to real CT scans. Synthesis was further enhanced by introducing perceptual loss, which improved structural details and visual quality, and incorporated 2.5D strategies to balance between 2D and 3D synthesis. Additionally, we emphasized a rigorous validation process using task-specific metrics, challenging traditional intensity-based and global metrics by focusing on the accurate reconstruction of anatomical structures. In the final stage, we developed a robust and scalable 3D synthesis framework by adapting nnU-Net for CT generation, along with an anatomical feature-prioritized loss function, enabling superior reconstruction of critical structures such as airways and vessels. Our work highlights the potential of deep learning-based models for generating high-quality synthetic CT images from UTE-MRI, offering a significant improvement in non-invasive lung imaging. These advances could greatly enhance the clinical applicability of UTE-MRI, providing a safer alternative to CT for the follow-up of chronic lung diseases. Furthermore, a patent is currently in preparation for the adoption of our method, paving the way for potential clinical use
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"Cross-modality semantic integration and robust interpretation of multimodal user interactions." Thesis, 2010. http://library.cuhk.edu.hk/record=b6075023.

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Multimodal systems can represent and manipulate semantics from different human communication modalities at different levels of abstraction, in which multimodal integration is required to integrate the semantics from two or more modalities and generate an interpretable output for further processing. In this work, we develop a framework pertaining to automatic cross-modality semantic integration of multimodal user interactions using speech and pen gestures. It begins by generating partial interpretations for each input event as a ranked list of hypothesized semantics. We devise a cross-modality semantic integration procedure to align the pair of hypothesis lists between every speech input event and every pen input event in a multimodal expression. This is achieved by the Viterbi alignment that enforces the temporal ordering and semantic compatibility constraints of aligned events. The alignment enables generation of a unimodal paraphrase that is semantically equivalent to the original multimodal expression. Our experiments are based on a multimodal corpus in the navigation domain. Application of the integration procedure to manual transcripts shows that correct unimodal paraphrases are generated for around 96% of the multimodal inquiries in the test set. However, if we replace this with automatic speech and pen recognition transcripts, the performance drops to around 53% of the test set. In order to address this issue, we devised the hypothesis rescoring procedure that evaluates all candidates of cross-modality integration derived from multiple recognition hypotheses from each modality. The rescoring function incorporates the integration score, N-best purity of recognized spoken locative references (SLRs), as well as distances between coordinates of recognized pen gestures and their interpreted icons on the map. Application of cross-modality hypothesis rescoring improved the performance to generate correct unimodal paraphrases for over 72% of the multimodal inquiries of the test set.
We have also performed a latent semantic modeling (LSM) for interpreting multimodal user input consisting of speech and pen gestures. Each modality of a multimodal input carries semantics related to a domain-specific task goal (TG). Each input is annotated manually with a TG based on the semantics. Multimodal input usually has a simpler syntactic structure and different order of semantic constituents from unimodal input. Therefore, we proposed to use LSM to derive the latent semantics from the multimodal inputs. In order to achieve this, we characterized the cross-modal integration pattern as 3-tuple multimodal terms taking into account SLR, pen gesture type and their temporal relation. The correlation term matrix is then decomposed using singular value decomposition (SVD) to derive the latent semantics automatically. TG inference on disjoint test set based on the latent semantics achieves accurate performance for 99% of the multimodal inquiries.
Hui, Pui Yu.
Adviser: Helen Meng.
Source: Dissertation Abstracts International, Volume: 73-02, Section: B, page: .
Thesis (Ph.D.)--Chinese University of Hong Kong, 2010.
Includes bibliographical references (leaves 294-306).
Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [201-] System requirements: Adobe Acrobat Reader. Available via World Wide Web.
Abstract also in Chinese.
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Book chapters on the topic "Cross-modality Translation"

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Zhang, Ran, Laetitia Meng-Papaxanthos, Jean-Philippe Vert, and William Stafford Noble. "Semi-supervised Single-Cell Cross-modality Translation Using Polarbear." In Lecture Notes in Computer Science, 20–35. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04749-7_2.

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Kang, Bogyeong, Hyeonyeong Nam, Ji-Wung Han, Keun-Soo Heo, and Tae-Eui Kam. "Multi-view Cross-Modality MR Image Translation for Vestibular Schwannoma and Cochlea Segmentation." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 100–108. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_10.

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Yang, Tao, and Lisheng Wang. "Koos Classification of Vestibular Schwannoma via Image Translation-Based Unsupervised Cross-Modality Domain Adaptation." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 59–67. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_6.

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Zhao, Ziyuan, Kaixin Xu, Huai Zhe Yeo, Xulei Yang, and Cuntai Guan. "MS-MT: Multi-scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation." In Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 68–78. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44153-0_7.

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Zhu, Lei, Ling Ling Chan, Teck Khim Ng, Meihui Zhang, and Beng Chin Ooi. "Deep Co-Training for Cross-Modality Medical Image Segmentation." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230633.

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Due to the expensive segmentation annotation cost, cross-modality medical image segmentation aims to leverage annotations from a source modality (e.g. MRI) to learn a model for target modality (e.g. CT). In this paper, we present a novel method to tackle cross-modality medical image segmentation as semi-supervised multi-modal learning with image translation, which learns better feature representations and is more robust to source annotation scarcity. For semi-supervised multi-modal learning, we develop a deep co-training framework. We address the challenges of co-training on divergent labeled and unlabeled data distributions with a theoretical analysis on multi-view adaptation and propose decomposed multi-view adaptation, which shows better performance than a naive adaptation method on concatenated multi-view features. We further formulate inter-view regularization to alleviate overfitting in deep networks, which regularizes deep co-training networks to be compatible with the underlying data distribution. We perform extensive experiments to evaluate our framework. Our framework significantly outperforms state-of-the-art domain adaptation methods on three segmentation datasets, including two public datasets on cross-modality cardiac substructure segmentation and abdominal multi-organ segmentation and one large scale private dataset on cross-modality brain tissue segmentation. Our code is publicly available at https://github.com/zlheui/DCT.
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Conference papers on the topic "Cross-modality Translation"

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Li, Yingtai, Shuo Yang, Xiaoyan Wu, Shan He, and S. Kevin Zhou. "Taming Stable Diffusion for MRI Cross-Modality Translation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2134–41. IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822349.

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Hassanzadeh, Reihaneh, Anees Abrol, Hamid Reza Hassanzadeh, and Vince D. Calhoun. "Cross-Modality Translation with Generative Adversarial Networks to Unveil Alzheimer’s Disease Biomarkers." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 1–4. IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10781737.

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Xiang, Yixin, Xianhua Zeng, Dajiang Lei, and Tao Fu. "MOADM: Manifold Optimization Adversarial Diffusion Model for Cross-Modality Medical Image Translation." In 2024 IEEE International Conference on Medical Artificial Intelligence (MedAI), 380–85. IEEE, 2024. https://doi.org/10.1109/medai62885.2024.00057.

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Zhao, Pu, Hong Pan, and Siyu Xia. "MRI-Trans-GAN: 3D MRI Cross-Modality Translation." In 2021 40th Chinese Control Conference (CCC). IEEE, 2021. http://dx.doi.org/10.23919/ccc52363.2021.9550256.

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Qi, Jinwei, and Yuxin Peng. "Cross-modal Bidirectional Translation via Reinforcement Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/365.

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The inconsistent distribution and representation of image and text make it quite challenging to measure their similarity, and construct correlation between them. Inspired by neural machine translation to establish a corresponding relationship between two entirely different languages, we attempt to treat images as a special kind of language to provide visual descriptions, so that translation can be conduct between bilingual pair of image and text to effectively explore cross-modal correlation. Thus, we propose Cross-modal Bidirectional Translation (CBT) approach, and further explore the utilization of reinforcement learning to improve the translation process. First, a cross-modal translation mechanism is proposed, where image and text are treated as bilingual pairs, and cross-modal correlation can be effectively captured in both feature spaces of image and text by bidirectional translation training. Second, cross-modal reinforcement learning is proposed to perform a bidirectional game between image and text, which is played as a round to promote the bidirectional translation process. Besides, both inter-modality and intra-modality reward signals can be extracted to provide complementary clues for boosting cross-modal correlation learning. Experiments are conducted to verify the performance of our proposed approach on cross-modal retrieval, compared with 11 state-of-the-art methods on 3 datasets.
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Tang, Shi, Xinchen Ye, Fei Xue, and Rui Xu. "Cross-Modality depth Estimation via Unsupervised Stereo RGB-to-infrared Translation." In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023. http://dx.doi.org/10.1109/icassp49357.2023.10095982.

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Ye, Jinhui, Wenxiang Jiao, Xing Wang, Zhaopeng Tu, and Hui Xiong. "Cross-modality Data Augmentation for End-to-End Sign Language Translation." In Findings of the Association for Computational Linguistics: EMNLP 2023. Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.findings-emnlp.904.

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Maji, Prasenjit, Kunal Dhibar, and Hemanta Kumar Mondal. "Revolutionizing and Enhancing Medical Diagnostics with Conditional GANs for Cross-Modality Image Translation." In 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom). IEEE, 2024. http://dx.doi.org/10.23919/indiacom61295.2024.10498844.

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Xu, Siwei, Junhao Liu, and Jing Zhang. "scACT: Accurate Cross-modality Translation via Cycle-consistent Training from Unpaired Single-cell Data." In CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management, 2722–31. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3627673.3679576.

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Cheng, Xize, Tao Jin, Rongjie Huang, Linjun Li, Wang Lin, Zehan Wang, Ye Wang, Huadai Liu, Aoxiong Yin, and Zhou Zhao. "MixSpeech: Cross-Modality Self-Learning with Audio-Visual Stream Mixup for Visual Speech Translation and Recognition." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.01442.

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