Галерея 3413971

Галерея 3413971

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Label Embedding Online Hashing for Cross-Modal Retrieval
Published: 12 October 2020 Publication History
MM '20: Proceedings of the 28th ACM International Conference on Multimedia
Overall Acceptance Rate 946 of 3,859 submissions, 25%

Sponsor:

sigmm

Ottawa ,

ON ,

Canada

Funding Sources Fundamental Research Funds of Shandong University Shandong Provincial Key Research and Development Program National Natural Science Foundation of China
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3394171.3413971.mp4
The topic of this presentation is "Label Embedding Online Hashing for Cross-Modal Retrieval". It first introduces the backgrounds of this work, and give the proposed method with two sub-parts, and experiments, finally, the conclusion. In our paper, we propose a novel label embedding online hashing method for cross-modal retrieval, termed as LEMON, which fully exploits the semantic labels by a well-designed label embedding framework. It also periodically considers the correlation between new and existing data by a block similarity matrix. Finally it solves the optimization problem through an efficient and discrete algorithm. Extensive experimental results on three benchmark datasets demonstrate that LEMON outperforms some state-of-the-art offline and online cross-modal hashing methods in terms of accuracy and efficiency.

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Supervised cross-modal hashing has gained a lot of attention recently. However, most existing methods learn binary codes or hash functions in a batch-based scheme, which is inefficient in an online scenario, i.e., data points come in a streaming fashion. Online hashing is a promising solution; however, there still exist several challenges, e.g., how to effectively exploit semantic information, how to discretely solve the binary optimization problem, how to efficiently update hash codes and hash functions. To address these issues, in this paper, we propose a novel supervised online cross-modal hashing method, i.e., Label EMbedding ONline hashing, LEMON for short. It builds a label embedding framework including label similarity preserving and label reconstructing, which may generate discriminative binary codes and reduce the computational complexity. Furthermore, it not only preserves the pairwise similarity of incoming data, but also establishes a connection between newly coming data and existing data by the inner product minimization on a block similarity matrix. In the light of this, it can exploit more similarity information and make the optimization less sensitive to incoming data, leading to effective binary codes. In addition, we design a discrete optimization algorithm to solve the binary optimization problem without relaxation. Therefore, the quantization error can be reduced. Moreover, its computational complexity is only relevant to the size of incoming data, making it very efficient and scalable to large-scale datasets. Extensive experimental results on three benchmark datasets demonstrate that LEMON outperforms some state-of-the-art offline and online cross-modal hashing methods in terms of accuracy and efficiency.
The topic of this presentation is "Label Embedding Online Hashing for Cross-Modal Retrieval". It first introduces the backgrounds of this work, and give the proposed method with two sub-parts, and experiments, finally, the conclusion. In our paper, we propose a novel label embedding online hashing method for cross-modal retrieval, termed as LEMON, which fully exploits the semantic labels by a well-designed label embedding framework. It also periodically considers the correlation between new and existing data by a block similarity matrix. Finally it solves the optimization problem through an efficient and discrete algorithm. Extensive experimental results on three benchmark datasets demonstrate that LEMON outperforms some state-of-the-art offline and online cross-modal hashing methods in terms of accuracy and efficiency.
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Chinese University of Hong Kong, Shenzhen, China
UNITN & Fondazione Bruno Kessler, Italy
National University of Singapore, Singapore
Association for Computing Machinery
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3394171. 3413971 .mp4. The topic of this presentation is "Label Embedding Online Hashing for Cross-Modal Retrieval". It first introduces the backgrounds of this work, and give the proposed method with two sub-parts, and experiments, finally, the conclusion. In our paper, we propose a novel label embedding online hashing method for cross-modal ...
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3394171. 3413971 .mp4. The topic of this presentation is "Label Embedding Online Hashing for Cross-Modal Retrieval". It first introduces the backgrounds of this work, and give the proposed method with two sub-parts, and experiments, finally, the conclusion. In our paper, we propose a novel label embedding online hashing method for cross-modal ...
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3394171. 3413971 .mp4. The topic of this presentation is "Label Embedding Online Hashing for Cross-Modal Retrieval". It first introduces the backgrounds of this work, and give the proposed method with two sub-parts, and experiments, finally, the conclusion. In our paper, we propose a novel label embedding online hashing method for cross-modal ...
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Галерея 3413971

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