Hashing in the Zero Shot Framework with Domain Adaptation

Pachori, Shubham; Deshpande, Ameya; Raman, Shanmuganathan

doi:10.48550/arxiv.1702.01933

Cited by 1 publication

(7 citation statements)

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“…AwA dataset contains 50 animals species. For fair comparison, we following the setting of the most similar work domain adaptation zero-shot hashing (DA-ZSH) [23], which also uses the unlabeled data of the novel classes. Specially, 10 classes are selected as the target classes and the rest 40 classes as the seen classes.…”

Section: Results On Awa Datasetmentioning

confidence: 99%

“…The comparison results are shown in Table 1. Note that the results of other methods are directly cited from the paper [23], which also uses the features extracted from the deep network. Again, our method yields the highest accuracy and beats all the baselines.…”

Section: Results On Awa Datasetmentioning

confidence: 99%

“…Guo et al [28] proposed transductive zero-shot recognition via jointly learning the shared model space for transferring the knowledge between the classes. The most similar work is [23], which formulated it as the domain adaptation problem for zeroshot hashing. Given the features of a mini-batch of images belong to the unseen classes, it updates the transformation matrix learned from the seen classes in each iteration.…”

Section: Related Workmentioning

confidence: 99%

“…For all deep learning-based methods, the weights of layers are initialized by the pre-trained AlexNet model 4 unless noted otherwise. IMH [27], COSDISH [8], SDH [26], ZSH [35], One-Stage Hashing [12] and Domain Adaptation Zero-shot Hashing (DA-ZSH) [23] are selected as the baselines.…”

Section: Datasets and Experimental Settingsmentioning

confidence: 99%

“…However, due to source classes and target classes have different data distributions, using the hash functions learned from the source dataset without any adaptation to the target dataset may causes an unknown bias, which is called projection domain shift problem [1,24]. Pachori et al [23] introduced an unsupervised domain-adaptation model for ZSH, which updated the learned hash model by the query data from the target classes.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Transductive Zero-Shot Hashing via Coarse-to-Fine Similarity Mining

Lai

2018

Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval

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Zero-shot Hashing (ZSH) is to learn hashing models for novel/target classes without training data, which is an important and challenging problem. Most existing ZSH approaches exploit transfer learning via an intermediate shared semantic representations between the seen/source classes and novel/target classes. However, due to having disjoint, the hash functions learned from the source dataset are biased when applied directly to the target classes. In this paper, we study the transductive ZSH, i.e., we have unlabeled data for novel classes. We put forward a simple yet efficient joint learning approach via coarse-to-fine similarity mining which transfers knowledges from source data to target data. It mainly consists of two building blocks in the proposed deep architecture: 1) a shared two-streams network, which the first stream operates on the source data and the second stream operates on the unlabeled data, to learn the effective common image representations, and 2) a coarse-to-fine module, which begins with finding the most representative images from target classes and then further detect similarities among these images, to transfer the similarities of the source data to the target data in a greedy fashion. Extensive evaluation results on several benchmark datasets demonstrate that the proposed hashing method achieves significant improvement over the state-of-the-art methods.

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Section: Results On Awa Datasetmentioning

confidence: 99%

Section: Results On Awa Datasetmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%