| Paper ID | IVMSP-26.6 |
| Paper Title |
WEBLY SUPERVISED DEEP ATTENTIVE QUANTIZATION |
| Authors |
Jinpeng Wang, Bin Chen, Tao Dai, Shutao Xia, Tsinghua University, China |
| Session | IVMSP-26: Attention for Vision |
| Location | Gather.Town |
| Session Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation Time: | Thursday, 10 June, 16:30 - 17:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVARS] Image & Video Analysis, Synthesis, and Retrieval |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
Learning to hash has been widely applied in large-scale image retrieval. Although current deep hashing methods yield state-of-the-art performance, their heavy dependence on ground-truth information actually makes it difficult to deploy in practical applications such as social media. To solve this problem, we propose a novel method termed Webly Supervised Deep Attentive Quantization (WSDAQ), where deep quantization is trained on web images associated with some user-provided weak tags, without consulting any ground-truth labels. Specifically, we design a tag processing module to leverage semantic information of tags so as to better supervised quantization learning. Besides, we propose an end-to-end trainable Attentive Product Quantization Module (APQM) to quantize deep features of images. Furthermore, we use a noise-contrastive estimation loss to train the model from the perspective of contrastive learning. Experiments validate that WSDAQ is superior to state-of-the-art baselines in compact coding trained on weakly-tagged web images. |
