Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval

• URL: http://arxiv.org/abs/2211.14515v1
• Date: Sat, 26 Nov 2022 08:50:08 GMT
• Title: Instance-level Heterogeneous Domain Adaptation for Limited-labeled Sketch-to-Photo Retrieval
• Authors: Fan Yang, Yang Wu, Zheng Wang, Xiang Li, Sakriani Sakti, Satoshi Nakamura
• Abstract summary: We propose an Instance-level Heterogeneous Domain Adaptation (IHDA) framework. We apply the fine-tuning strategy for identity label learning, aiming to transfer the instance-level knowledge in an inductive transfer manner. Experiments show that our method has set a new state of the art on three sketch-to-photo image retrieval benchmarks without extra annotations.
• Score: 36.32367182571164
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Although sketch-to-photo retrieval has a wide range of applications, it is costly to obtain paired and rich-labeled ground truth. Differently, photo retrieval data is easier to acquire. Therefore, previous works pre-train their models on rich-labeled photo retrieval data (i.e., source domain) and then fine-tune them on the limited-labeled sketch-to-photo retrieval data (i.e., target domain). However, without co-training source and target data, source domain knowledge might be forgotten during the fine-tuning process, while simply co-training them may cause negative transfer due to domain gaps. Moreover, identity label spaces of source data and target data are generally disjoint and therefore conventional category-level Domain Adaptation (DA) is not directly applicable. To address these issues, we propose an Instance-level Heterogeneous Domain Adaptation (IHDA) framework. We apply the fine-tuning strategy for identity label learning, aiming to transfer the instance-level knowledge in an inductive transfer manner. Meanwhile, labeled attributes from the source data are selected to form a shared label space for source and target domains. Guided by shared attributes, DA is utilized to bridge cross-dataset domain gaps and heterogeneous domain gaps, which transfers instance-level knowledge in a transductive transfer manner. Experiments show that our method has set a new state of the art on three sketch-to-photo image retrieval benchmarks without extra annotations, which opens the door to train more effective models on limited-labeled heterogeneous image retrieval tasks. Related codes are available at \url{https://github.com/fandulu/IHDA.

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