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TelecomNet : Tag-Based Weakly-Supervised Modally Cooperative Hashing Network for Image Retrieval

We are concerned with using user-tagged images to learn proper hashing functions for image retrieval. The benefits are two-fold: (1) we could obtain abundant training data for deep hashing models; (2) tagging data possesses richer semantic information which could help better characterize similarity relationships between images. However, tagging data suffers from noises, vagueness and incompleteness. Different from previous unsupervised or supervised hashing learning, we propose a novel weakly-supervised deep hashing framework which consists of two stages: weakly-supervised pre-training and supervised fine-tuning. The second stage is as usual. In the first stage, we propose two formulations Tag-basEd weakLy-supErvised Modally COoperative hashing Network (TelecomNet) and Generalized TelecomNet (GTelecomNet). Rather than performing supervision on tags, TelecomNet first learns an observed semantic embedding vector for each image from attached tags and then uses it to guide hashing learning. GTelecomNet introduces a novel semantic network to exploit more precise semantic information. By carefully designing the optimization problem, they can well leverage tagging information and image content for hashing learning. The framework is general and does not depend on specific deep hashing methods. Empirical results on real world datasets show that they significantly increase the performance of state-of-the-art deep hashing methods.

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Informasi Detail

Judul Seri

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE; Vol.44 No.11 Part 1 November 2022

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 7940-7954

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

IMAGE RETRIEVAL
WEAKLY-SUPERVISED LEARNING
MULTI-MODAL LEARNING
MULTIMEDIA RETRIEVAL

Info Detail Spesifik

DOI: 10.1109/TPAMI.2021.3114089

Pernyataan Tanggungjawab

Wei Zhao, Cai Xu, Ziyu Guan, Xunlian Wu, Wanqing Zhao, Qiguang Miao, Xiaofei He, Quan Wang

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