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Relevant bibliographies by topics / Zero-shot Retrieval

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Journal articles

Academic literature on the topic 'Zero-shot Retrieval'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Zero-shot Retrieval"

1

Dutta, Titir, and Soma Biswas. "Generalized Zero-Shot Cross-Modal Retrieval." IEEE Transactions on Image Processing 28, no. 12 (2019): 5953–62. http://dx.doi.org/10.1109/tip.2019.2923287.

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2

Seo, Sanghyun, and Juntae Kim. "Hierarchical Semantic Loss and Confidence Estimator for Visual-Semantic Embedding-Based Zero-Shot Learning." Applied Sciences 9, no. 15 (2019): 3133. http://dx.doi.org/10.3390/app9153133.

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Traditional supervised learning is dependent on the label of the training data, so there is a limitation that the class label which is not included in the training data cannot be recognized properly. Therefore, zero-shot learning, which can recognize unseen-classes that are not used in training, is gaining research interest. One approach to zero-shot learning is to embed visual data such as images and rich semantic data related to text labels of visual data into a common vector space to perform zero-shot cross-modal retrieval on newly input unseen-class data. This paper proposes a hierarchical

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3

Wang, Xiao, Craig Macdonald, and Iadh Ounis. "Improving zero-shot retrieval using dense external expansion." Information Processing & Management 59, no. 5 (2022): 103026. http://dx.doi.org/10.1016/j.ipm.2022.103026.

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4

Kumar, Sanjeev. "Phase retrieval with physics informed zero-shot network." Optics Letters 46, no. 23 (2021): 5942. http://dx.doi.org/10.1364/ol.433625.

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5

Lin, Kaiyi, Xing Xu, Lianli Gao, Zheng Wang, and Heng Tao Shen. "Learning Cross-Aligned Latent Embeddings for Zero-Shot Cross-Modal Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11515–22. http://dx.doi.org/10.1609/aaai.v34i07.6817.

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Zero-Shot Cross-Modal Retrieval (ZS-CMR) is an emerging research hotspot that aims to retrieve data of new classes across different modality data. It is challenging for not only the heterogeneous distributions across different modalities, but also the inconsistent semantics across seen and unseen classes. A handful of recently proposed methods typically borrow the idea from zero-shot learning, i.e., exploiting word embeddings of class labels (i.e., class-embeddings) as common semantic space, and using generative adversarial network (GAN) to capture the underlying multimodal data structures, as

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6

Zhang, Haofeng, Yang Long, and Ling Shao. "Zero-shot Hashing with orthogonal projection for image retrieval." Pattern Recognition Letters 117 (January 2019): 201–9. http://dx.doi.org/10.1016/j.patrec.2018.04.011.

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7

Zhang, Zhaolong, Yuejie Zhang, Rui Feng, Tao Zhang, and Weiguo Fan. "Zero-Shot Sketch-Based Image Retrieval via Graph Convolution Network." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12943–50. http://dx.doi.org/10.1609/aaai.v34i07.6993.

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Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) has been proposed recently, putting the traditional Sketch-based Image Retrieval (SBIR) under the setting of zero-shot learning. Dealing with both the challenges in SBIR and zero-shot learning makes it become a more difficult task. Previous works mainly focus on utilizing one kind of information, i.e., the visual information or the semantic information. In this paper, we propose a SketchGCN model utilizing the graph convolution network, which simultaneously considers both the visual information and the semantic information. Thus, our model can e

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8

Yang, Fan, Zheng Wang, Jing Xiao, and Shin'ichi Satoh. "Mining on Heterogeneous Manifolds for Zero-Shot Cross-Modal Image Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 12589–96. http://dx.doi.org/10.1609/aaai.v34i07.6949.

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Most recent approaches for the zero-shot cross-modal image retrieval map images from different modalities into a uniform feature space to exploit their relevance by using a pre-trained model. Based on the observation that manifolds of zero-shot images are usually deformed and incomplete, we argue that the manifolds of unseen classes are inevitably distorted during the training of a two-stream model that simply maps images from different modalities into a uniform space. This issue directly leads to poor cross-modal retrieval performance. We propose a bi-directional random walk scheme to mining

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9

Xu, Rui, Zongyan Han, Le Hui, Jianjun Qian, and Jin Xie. "Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 2902–10. http://dx.doi.org/10.1609/aaai.v36i3.20195.

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Sketch-based 3D shape retrieval is a challenging task due to the large domain discrepancy between sketches and 3D shapes. Since existing methods are trained and evaluated on the same categories, they cannot effectively recognize the categories that have not been used during training. In this paper, we propose a novel domain disentangled generative adversarial network (DD-GAN) for zero-shot sketch-based 3D retrieval, which can retrieve the unseen categories that are not accessed during training. Specifically, we first generate domain-invariant features and domain-specific features by disentangl

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10

Xu, Xing, Jialin Tian, Kaiyi Lin, Huimin Lu, Jie Shao, and Heng Tao Shen. "Zero-shot Cross-modal Retrieval by Assembling AutoEncoder and Generative Adversarial Network." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1s (2021): 1–17. http://dx.doi.org/10.1145/3424341.

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Conventional cross-modal retrieval models mainly assume the same scope of the classes for both the training set and the testing set. This assumption limits their extensibility on zero-shot cross-modal retrieval (ZS-CMR), where the testing set consists of unseen classes that are disjoint with seen classes in the training set. The ZS-CMR task is more challenging due to the heterogeneous distributions of different modalities and the semantic inconsistency between seen and unseen classes. A few of recently proposed approaches are inspired by zero-shot learning to estimate the distribution underlyi

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