Rank-consistency multi-label deep hashing
Webb2 feb. 2024 · In this paper, we propose a novel deep hashing method for scalable multi-label image search. Unlike existing approaches with conventional objectives such as contrast and triplet losses, we employ a rank list, rather than pairs or triplets, to provide sufficient global supervision information for all the samples. Specifically, a new rank ... Webb14 aug. 2024 · Extensive experiments on four standard multi-label benchmarks justify the proposed method outperforms the state-of-the-art, is robust among different hash bits …
Rank-consistency multi-label deep hashing
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Webbframework. Deep Semantic Ranking Hashing (DSRH) [26] learns the hash functions by preserving semantic similarity between multi-label images. Other ranking-based deep hashing methods have also been proposed in recent years [18, 22]. Besides the triplet ranking based methods, some pairwise label based deep hashing methods are also … Webb10 jan. 2024 · Recently, deep supervised hashing methods have become popular for large-scale image retrieval task. To preserve the semantic similarity notion between examples, they typically utilize the...
WebbTo 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., ... WebbDeep 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.
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. 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 …
Webb6 nov. 2024 · Due to its low storage cost and fast query speed, hashing has been widely applied to approximate nearest neighbor search for large-scale image retrieval, while deep hashing further improves the retrieval quality by learning a good image representation. However, existing deep hash methods simplify multi-label images into single-label …
Webbfectively 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 … download bt cloud voice appWebb12 juni 2015 · Deep semantic ranking based hashing for multi-label image retrieval Abstract: With the rapid growth of web images, hashing has received increasing … download btd5Webb28 okt. 2024 · Rank-Consistency Deep Hashing for Scalable Multi-Label Image Search. Abstract:As hashing becomes an increasingly appealing technique for large-scale image … download btcWebbDeep Rank Cross-Modal Hashing with Semantic Consistent for Image-Text Retrieval. Abstract: Cross-modal hashing retrieval approaches maps heterogeneous multi-modal … download btc toolsWebb8 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 instance similarity is quantified into a percentage based on the normalized semantic labels. clark fosterWebb1 juli 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 … download bt cloud work phoneWebbods 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 … download btc software