Rank-consistency multi-label deep hashing
Webb1 juli 2024 · This paper proposes a novel deep hashing method for scalable multi-label image search that employs a rank list, rather than pairs or triplets, to provide sufficient … Webbods consider the high-order ranking information for hashing learning. For example, deep semantic ranking based hash-ing (Zhao et al. 2015) learns deep hash functions based on CNN (Convolutional neural network)(Krizhevsky, Sutskever, and Hinton 2012), which preserves the semantic structure of multi-label images. Simultaneous feature learning …
Rank-consistency multi-label deep hashing
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WebbExtensive experiments on public multilabel datasets demonstrate that (1) LAH can achieve the state-of-the-art retrieval results and (2) the usage of co-occurrence relationship and … Webb1 juli 2024 · In this paper, we present a deep hashing method for multi-label image retrieval, which uses a rank list to provide global supervision information. Unlike most …
WebbDeep Rank Cross-Modal Hashing with Semantic Consistent for Image-Text Retrieval. Abstract: Cross-modal hashing retrieval approaches maps heterogeneous multi-modal … WebbIn this paper, a hashing method called Deep Adversarial Discrete Hashing (DADH) is proposed to address these issues for cross-modal retrieval. The proposed method uses adversarial training to learn features across modalities and ensure the distribution consistency of feature representations across modalities.
Webb8 mars 2024 · In this paper, a new deep hashing method is proposed for multi-label image retrieval by re-defining the pairwise similarity into an instance similarity, where the … WebbDeep Polarization Reconstruction with PDAVIS Events Haiyang Mei · Zuowen Wang · Xin Yang · Xiaopeng Wei · Tobi Delbruck Unsupervised space-time network for temporally-consistent segmentation of multiple motions Etienne Meunier · Patrick Bouthemy NeMo: Learning 3D Neural Motion Fields from Multiple Video Instances of the Same Action
Webb11 aug. 2024 · The existing multi-label image retrieval methods can be divided into two main categories: one is to use the region proposal module to extract foreground objects and generate multiple hash codes for a multi-lable image [13, 8, 22].The other is to consider the number of common labels of multi-label image as the metric of similarity …
WebbRank-Consistency Multi-Label Deep Hashing ICME 2024 ( Best Paper Award ) Cheng Ma, Zhixiang Chen, Jiwen Lu, Jie Zhou [Link] Nonlinear Discrete Hashing IEEE Transactions … mmc600isWebbDeep hashing methods have been intensively studied and successfully applied in massive fast image retrieval. However, inherited from the deficiency of deep neural networks, deep hashing models can be easily fooled by adversarial examples, which brings a serious security risk to hashing based retrieval. mmc07s1awwWebb11 aug. 2024 · A deep supervised hashing method for multi-label image retrieval is developed, in which it is proposed to learn a binary “mask” map that can identify the approximate locations of objects in an image, so that it can obtain length-limited hash codes which mainly focus on an image’s objects but ignore the background. 26 mmc-1gftlc.s10WebbTo address these issues, we propose a novel deep hashing method, termed multi-label hashing for dependency relations among multiple objectives (DRMH). ... [35] Ma C., Chen Z., Lu J., and Zhou J., “ Rank-consistency multi-label deep hashing,” in Proc. IEEE Int. Conf. Multimedia Expo., ... mmc 6400 section 901: media strategyWebb7 okt. 2024 · For multi-label image retrieval based on deep hashing, the ultimate challenge is to map from the original image to binary space while preserving high-level semantic … mmc act 1965WebbIn this paper, we propose an order-sensitive deep hashing for scalable medical image retrieval in the scenario of coexistence of multiple medical conditions. The pairwise similarity... mmc act 1949Webbfectively measured. Deep cross-modal hashing further im-proves the retrieval performance as the deep neural net-works can generate more semantic relevant features and hash codes. In this paper, we study the unsupervised deep cross-modal hash coding and propose Deep Joint-SemanticsReconstructingHashing(DJSRH),whichhasthe … mmc75a-3