Results 11 to 20 of about 64,981 (259)
A Survey of Full-Cycle Cross-Modal Retrieval: From a Representation Learning Perspective
Applied Sciences, 2023 Cross-modal retrieval aims to elucidate information fusion, imitate human learning, and advance the field. Although previous reviews have primarily focused on binary and real-value coding methods, there is a scarcity of techniques grounded in deep ...
Suping Wang +4 more
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Unsupervised Deep Relative Neighbor Relationship Preserving Cross-Modal Hashing
Mathematics, 2022 The image-text cross-modal retrieval task, which aims to retrieve the relevant image from text and vice versa, is now attracting widespread attention.
Xiaohan Yang +5 more
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Object Feature Based Deep Hashing for Cross-Modal Retrieval
Jisuanji kexue yu tansuo, 2021 With the rapid growth of data with different modalities on the Internet, cross-modal retrieval has gradually become a hot research topic. Due to its efficiency and effectiveness, Hashing based methods have become one of the most popular large-scale cross-
ZHU Jie, BAI Hongyu, ZHANG Zhongyu, XIE Bojun, ZHANG Junsan
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The State of the Art for Cross-Modal Retrieval: A Survey
IEEE Access, 2023 Cross-modal retrieval, which aims to search for semantically relevant data across different modalities, has received increasing attention in recent years.
Kun Zhou +2 more
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Deep Adversarial Cascaded Hashing for Cross-Modal Vessel Image Retrieval
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023 In recent years, cross-modal remote sensing image retrieval has attracted a lot of attention in remote sensing (RS) information processing. It is worth mentioning that land cover scenes, whether unimodal or cross-modal, are the primary research contents ...
Jiaen Guo, Xin Guan
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Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval
, 2023 Contrastive language-image pre-training (CLIP) has demonstrated remarkable zero-shot classification ability, namely image classification using novel text labels. Existing works have attempted to enhance CLIP by fine-tuning on downstream tasks, but these have inadvertently led to performance degradation on unseen classes, thus harming zero-shot ...
Eom, Seongha +3 more
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Variational recurrent sequence-to-sequence retrieval for stepwise illustration [PDF]
, 2020 We address and formalise the task of sequence-to-sequence (seq2seq) cross-modal retrieval. Given a sequence of text passages as query, the goal is to retrieve a sequence of images that best describes and aligns with the query.
Batra, Vishwas +5 more
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Binary Set Embedding for Cross-Modal Retrieval [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2017 Cross-modal retrieval is such a challenging topic that traditional global representations would fail to bridge the semantic gap between images and texts to a satisfactory level. Using local features from images and words from documents directly can be more robust for the scenario with large intraclass variations and small interclass discrepancies.
Mengyang Yu, Li Liu, Ling Shao
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Cross‐modal semantic correlation learning by Bi‐CNN network
IET Image Processing, 2021 Cross modal retrieval can retrieve images through a text query and vice versa. In recent years, cross modal retrieval has attracted extensive attention.
Chaoyi Wang +5 more
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Deep Adversarial Learning Triplet Similarity Preserving Cross-Modal Retrieval Algorithm
Mathematics, 2022 The cross-modal retrieval task can return different modal nearest neighbors, such as image or text. However, inconsistent distribution and diverse representation make it hard to directly measure the similarity relationship between different modal samples,
Guokun Li +6 more
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