Semi-Relaxation Supervised Hashing for Cross-Modal Retrieval

Zhang, Pengfei; Li, Chuan-Xiang; Liu, Meng‐Yuan; Nie, Liqiang; Xu, Xin-Shun

doi:10.1145/3123266.3123320

Cited by 45 publications

(8 citation statements)

References 27 publications

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“…The proposed framework is built based upon the inner product based formulation [12], [36], [41], [42], [43]. The key ingredient is to map data points into binary codes, the inner product of which can well approximate the similarity matrix S t ∈ {−1, +1} nt×nt where S t ij = 1, if l t i = l t j , and −1 otherwise.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…The above formulation has been shown to be effective in traditional offline hashing methods [12], [41], [42], [43]. However, directly applying this equation to online learning is infeasible due to the "data imbalance" problem, as identified in [36].…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…T RADITIONAL hashing methods [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23] are mostly designed to learn hash functions offline from a fixed collection of training data with/without supervised labels. However, such a setting cannot handle dynamic application scenarios where data are fed into the system in a streaming fashion.…”

Section: Introductionmentioning

confidence: 99%

“…To address the above issues, in this paper, we propose a novel supervised online hashing method, dubbed Fast Class-wise Updating for Online Hashing (FCOH). Our key innovation is introducing the usage of inner product to preserve the similarity relationship in the Hamming space, which has been demonstrated to be very effective in learning binary codes offline [12], [36], [41], [42], [43]. Unlike previous inner product based online hashing [36], we address the defects in online adaptivity and training efficiency in a unified framework.…”

Section: Introductionmentioning

confidence: 99%

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Fast Class-wise Updating for Online Hashing

Lin¹,

Ji²,

Sun³

et al. 2020

Preprint

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Online image hashing has received increasing research attention recently, which processes large-scale data in a streaming fashion to update the hash functions on-the-fly. To this end, most existing works exploit this problem under a supervised setting, i.e., using class labels to boost the hashing performance, which suffers from the defects in both adaptivity and efficiency: First, large amounts of training batches are required to learn up-to-date hash functions, which leads to poor online adaptivity. Second, the training is timeconsuming, which contradicts with the core need of online learning. In this paper, a novel supervised online hashing scheme, termed Fast Class-wise Updating for Online Hashing (FCOH), is proposed to address the above two challenges by introducing a novel and efficient inner product operation. To achieve fast online adaptivity, a class-wise updating method is developed to decompose the binary code learning and alternatively renew the hash functions in a class-wise fashion, which well addresses the burden on large amounts of training batches. Quantitatively, such a decomposition further leads to at least 75% storage saving. To further achieve online efficiency, we propose a semi-relaxation optimization, which accelerates the online training by treating different binary constraints independently. Without additional constraints and variables, the time complexity is significantly reduced. Such a scheme is also quantitatively shown to well preserve past information during updating hashing functions. We have quantitatively demonstrated that the collective effort of class-wise updating and semi-relaxation optimization provides a superior performance comparing to various state-of-the-art methods, which is verified through extensive experiments on three widely-used datasets.

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Section: The Proposed Methodsmentioning

confidence: 99%

Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fast Class-wise Updating for Online Hashing

Lin¹,

Ji²,

Sun³

et al. 2020

Preprint

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“…In this way, supervised cross-modal hashing methods have better performance than unsupervised cross-modal hashing methods. Representative supervised cross-modal hashing methods include Semantic Preserving Hashing (SePH) [14], Semantic Correlation Maximization (SCM) [15], Semi-Relaxation Supervised Hashing (SRSH) [16] and Dictionary Learning Cross-modal Hashing (DLCMH) [17]. However, almost all these existing cross-modal hashing methods are based on shadow architectures and hand-crafted features.…”

Section: Introductionmentioning

confidence: 99%

Self-Attention and Adversary Guided Hashing Network for Cross-Modal Retrieval

Chen¹,

Wang²,

Wu³

2020

Preprint

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Recently deep cross-modal hashing networks have received increasing interests due to its superior query efficiency and low storage cost. However, most of existing methods concentrate less on hash representations learning part, which means the semantic information of data cannot be fully used. Furthermore, they may neglect the high-ranking relevance and consistency of hash codes. To solve these problems, we propose a Self-Attention and Adversary Guided Hashing Network (SAAGHN). Specifically, it employs self-attention mechanism in hash representations learning part to extract rich semantic relevance information. Meanwhile, in order to keep invariability of hash codes, adversarial learning is adopted in the hash codes learning part. In addition, to generate higher-ranking hash codes and avoid local minima early, a new batch semi-hard cosine triplet loss and a cosine quantization loss are proposed. Extensive experiments on two benchmark datasets have shown that SAAGHN outperforms other baselines and achieves the state-of-the-art performance.

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