2021 IEEE International Conference on Acoustics, Speech and Signal Processing
6-11 June 2021 • Toronto, Ontario, Canada
Extracting Knowledge from Information
| 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 | ||
| 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. | ||