Weakly-Supervised Online Hashing
- URL: http://arxiv.org/abs/2009.07436v2
- Date: Thu, 8 Jul 2021 13:30:36 GMT
- Title: Weakly-Supervised Online Hashing
- Authors: Yu-Wei Zhan, Xin Luo, Yu Sun, Yongxin Wang, Zhen-Duo Chen, Xin-Shun Xu
- Abstract summary: We propose a new hashing method named Weakly-supervised Online Hashing (WOH)
In order to learn high-quality hash codes, WOH exploits the weak supervision by considering the semantics of tags and removing the noise.
We develop a discrete online optimization algorithm for WOH, which is efficient and scalable.
- Score: 17.987362068956028
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: With the rapid development of social websites, recent years have witnessed an
explosive growth of social images with user-provided tags which continuously
arrive in a streaming fashion. Due to the fast query speed and low storage
cost, hashing-based methods for image search have attracted increasing
attention. However, existing hashing methods for social image retrieval are
based on batch mode which violates the nature of social images, i.e., social
images are usually generated periodically or collected in a stream fashion.
Although there exist many online image hashing methods, they either adopt
unsupervised learning which ignore the relevant tags, or are designed in the
supervised manner which needs high-quality labels. In this paper, to overcome
the above limitations, we propose a new method named Weakly-supervised Online
Hashing (WOH). In order to learn high-quality hash codes, WOH exploits the weak
supervision by considering the semantics of tags and removing the noise.
Besides, We develop a discrete online optimization algorithm for WOH, which is
efficient and scalable. Extensive experiments conducted on two real-world
datasets demonstrate the superiority of WOH compared with several
state-of-the-art hashing baselines.
Related papers
- Bit-mask Robust Contrastive Knowledge Distillation for Unsupervised
Semantic Hashing [71.47723696190184]
We propose an innovative Bit-mask Robust Contrastive knowledge Distillation (BRCD) method for semantic hashing.
BRCD is specifically devised for the distillation of semantic hashing models.
arXiv Detail & Related papers (2024-03-10T03:33:59Z) - Cascading Hierarchical Networks with Multi-task Balanced Loss for
Fine-grained hashing [1.6244541005112747]
Fine-grained hashing is more challenging than traditional hashing problems.
We propose a cascaded network to learn compact and highly semantic hash codes.
We also propose a novel approach to coordinately balance the loss of multi-task learning.
arXiv Detail & Related papers (2023-03-20T17:08:48Z) - Fast Online Hashing with Multi-Label Projection [15.85793225585693]
We propose a novel Fast Online Hashing (FOH) method which only updates the binary codes of a small part of the database.
The experimental results show that the proposed FOH can achieve dramatic superiority on query time up to 6.28 seconds less than state-of-the-art baselines.
arXiv Detail & Related papers (2022-12-03T03:19:28Z) - Weighted Contrastive Hashing [11.14153532458873]
Unsupervised hash development has been hampered by insufficient data similarity mining based on global-only image representations.
We introduce a novel mutual attention module to alleviate the problem of information asymmetry in network features caused by the missing image structure.
The aggregated weighted similarities, which reflect the deep image relations, are distilled to facilitate the hash codes learning with a distillation loss.
arXiv Detail & Related papers (2022-09-28T13:47:33Z) - PHPQ: Pyramid Hybrid Pooling Quantization for Efficient Fine-Grained
Image Retrieval [68.05570413133462]
We propose a Pyramid Hybrid Pooling Quantization (PHPQ) module to capture and preserve fine-grained semantic information from multi-level features.
Experiments on two widely-used public benchmarks, CUB-200-2011 and Stanford Dogs, demonstrate that PHPQ outperforms state-of-the-art methods.
arXiv Detail & Related papers (2021-09-11T07:21:02Z) - Fast Class-wise Updating for Online Hashing [196.14748396106955]
This paper presents a novel supervised online hashing scheme, termed Fast Class-wise Updating for Online Hashing (FCOH)
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.
To further achieve online efficiency, we propose a semi-relaxation optimization, which accelerates the online training by treating different binary constraints independently.
arXiv Detail & Related papers (2020-12-01T07:41:54Z) - Deep Reinforcement Learning with Label Embedding Reward for Supervised
Image Hashing [85.84690941656528]
We introduce a novel decision-making approach for deep supervised hashing.
We learn a deep Q-network with a novel label embedding reward defined by Bose-Chaudhuri-Hocquenghem codes.
Our approach outperforms state-of-the-art supervised hashing methods under various code lengths.
arXiv Detail & Related papers (2020-08-10T09:17:20Z) - Dual-level Semantic Transfer Deep Hashing for Efficient Social Image
Retrieval [35.78137004253608]
Social network stores and disseminates a tremendous amount of user shared images.
Deep hashing is an efficient indexing technique to support large-scale social image retrieval.
Existing methods suffer from severe semantic shortage when optimizing a large amount of deep neural network parameters.
We propose a Dual-level Semantic Transfer Deep Hashing (DSTDH) method to alleviate this problem.
arXiv Detail & Related papers (2020-06-10T01:03:09Z) - Reinforcing Short-Length Hashing [61.75883795807109]
Existing methods have poor performance in retrieval using an extremely short-length hash code.
In this study, we propose a novel reinforcing short-length hashing (RSLH)
In this proposed RSLH, mutual reconstruction between the hash representation and semantic labels is performed to preserve the semantic information.
Experiments on three large-scale image benchmarks demonstrate the superior performance of RSLH under various short-length hashing scenarios.
arXiv Detail & Related papers (2020-04-24T02:23:52Z) - A Survey on Deep Hashing Methods [52.326472103233854]
Nearest neighbor search aims to obtain the samples in the database with the smallest distances from them to the queries.
With the development of deep learning, deep hashing methods show more advantages than traditional methods.
Deep supervised hashing is categorized into pairwise methods, ranking-based methods, pointwise methods and quantization.
Deep unsupervised hashing is categorized into similarity reconstruction-based methods, pseudo-label-based methods and prediction-free self-supervised learning-based methods.
arXiv Detail & Related papers (2020-03-04T08:25:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.