Name
Title | ||
|---|---|---|
Instance-Level Heterogeneous Domain Adaptation For Limited-Labeled Sketch-To-Photo Retrieval |
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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TMM.2020.3009476 | IEEE TRANSACTIONS ON MULTIMEDIA |
Keywords | DocType | Volume |
Task analysis, Training data, Data models, Image retrieval, Connectors, Training, Entropy, Domain adaptation, cross-modal image retrieval, sketch, person re-identification | Journal | 23 |
ISSN | Citations | PageRank |
1520-9210 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fan Yang | 1 | 196 | 48.38 |
| Yang Wu | 2 | 84 | 18.42 |
| Zheng Wang | 3 | 352 | 36.33 |
| Xiang Li | 4 | 345 | 82.16 |
| Sakriani Sakti | 5 | 257 | 65.02 |
| Satoshi Nakamura | 6 | 1099 | 194.59 |