Name
Abstract | ||
|---|---|---|
Most recent approaches for the zero-shot cross-modal image retrieval map images from different modalities into a uniform feature space to exploit their relevance by using a pre-trained model. Based on the observation that manifolds of zero-shot images are usually deformed and incomplete, we argue that the manifolds of unseen classes are inevitably distorted during the training of a two-stream model that simply maps images from different modalities into a uniform space. This issue directly leads to poor cross-modal retrieval performance. We propose a bi-directional random walk scheme to mining more reliable relationships between images by traversing heterogeneous manifolds in the feature space of each modality. Our proposed method benefits from intra-modal distributions to alleviate the interference caused by noisy similarities in the cross-modal feature space. As a result, we achieved great improvement in the performance of the thermal v.s. visible image retrieval task. The code of this paper: https://github.com/fyang93/cross-modal-retrieval |
Year | Venue | DocType |
|---|---|---|
2020 | AAAI | Conference |
Volume | ISSN | Citations |
34 | 2159-5399 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang, Fan | 1 | 2 | 2.74 |
| Zheng Wang | 2 | 13 | 3.08 |
| Jing Xiao | 3 | 42 | 12.17 |
| Shin'ichi Satoh | 4 | 7 | 3.83 |