VALSE 论文速览 第93期：SEMICON—一种用于大规模细粒度图像检索的哈希学习算法 ...

论文题目：SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval

作者列表：沈阳 (南京理工大学)，孙旭豪 (南京理工大学)，魏秀参 (南京理工大学)，蒋庆远，杨健 (南京理工大学)

B站观看网址：

https://www.bilibili.com/video/BV1At4y137iQ/

论文摘要：

In this paper, we propose Suppression-Enhancing Mask based attention and Interactive Channel transformatiON (SEMICON)to learn binary hash codes for dealing with large-scale fine-grained image retrieval tasks. In SEMICON, we first develop a suppression-enhancing mask (SEM)based attention to dynamically localize discriminative image regions. More importantly, different from existing attention mechanism simply erasing previous discriminative regions, our SEM is developed to restrain such regions and then discover other complementary regions by considering the relation between activated regions in a stage-by-stage fashion. In each stage, the interactive channel transformation (ICON)module is afterwards designed to exploit correlations across channels of attended activation tensors. Since channels could generally correspond to the parts of fine-grained objects, the part correlation can be also modeled accordingly, which further improves fine-grained retrieval accuracy. Moreover, to be computational economy, ICON is realized by an efficient two-step process. Finally, the hash learning of our SEMICON consists of both global- and local-level branches for better representing fine-grained objects and then generating binary hash codes explicitly corresponding to multiple levels. Experiments on five benchmark fine-grained datasets show our superiority over competing methods.

论文信息：

[1] Yang Shen, Xuhao Sun, Xiu-Shen Wei*, Qing-Yuan Jiang, Jian Yang. SEMICON: A Learning-to-hash Solution for Large-scale Fine-grained Image Retrieval. ECCV 2022.

论文链接：

[http://www.weixiushen.com/publication/eccv22_SEMICON.pdf]

[http://www.weixiushen.com/publication/eccv22_SEMICON_supp.pdf]

代码链接：

[https://github.com/NJUST-VIPGroup/SEMICON]

视频讲者简介：

沈阳，南京理工大学博士生，主要研究方向为计算机视觉，师从魏秀参教授。在 ECCV、NeurIPS、IJCAI等国际会议发表多篇论文。获首届“计图”人工智能挑战赛狗细分类赛道冠军，在CVPR、ICCV等举办的国际权威细粒度视觉评测中多次获冠亚季军。

特别鸣谢本次论文速览主要组织者：

月度轮值AC：冯尊磊 (浙江大学)，徐易 (大连理工大学)

季度责任AC：张姗姗 (南京理工大学)