2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Login Paper Search My Schedule Paper Index Help

My ICIP 2021 Schedule

Note: Your custom schedule will not be saved unless you create a new account or login to an existing account.
  1. Create a login based on your email (takes less than one minute)
  2. Perform 'Paper Search'
  3. Select papers that you desire to save in your personalized schedule
  4. Click on 'My Schedule' to see the current list of selected papers
  5. Click on 'Printable Version' to create a separate window suitable for printing (the header and menu will appear, but will not actually print)

Paper Detail

Paper IDMLR-APPL-MDSP.10
Paper Title TWO-STREAM HYBRID ATTENTION NETWORK FOR MULTIMODAL CLASSIFICATION
Authors Qipin Chen, Pennsylvania State University, United States; Zhenyu Shi, Zhen Zuo, Jinmiao Fu, Yi Sun, Amazon LLC, United States
SessionMLR-APPL-MDSP: Machine learning for multidimensional signal processing
LocationArea F
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Multidimensional Signal Processing: Signal and system modeling and identification
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract On modern e-commerce platforms like Amazon, the number of products is fast growing, precise and efficient product classification becomes a key lever to great customer shopping experience. To tackle the large-scale product classification problem, a major challenge is how to leverage multimodal product information (e.g., image, text). One of the most successful directions is the attention-based deep multimodal learning, where there are mainly two types of frameworks: 1) keyless attention, which learns the importance of features within each modal; and 2) key-based attention, which learns the importance of features using other modalities. In this paper, we propose a novel Two-stream Hybrid Attention Network (HANet), which leverages both key-based and keyless attention mechanisms to capture the key information across product image and title modalities. We experimentally show that our HANet achieves state-of-the-art performance on Amazon-scale product classification problem.