  Live commerce is the act of selling products online through live streaming.
The customer's diverse demands for online products introduce more challenges to
Livestreaming Product Recognition. Previous works have primarily focused on
fashion clothing data or utilize single-modal input, which does not reflect the
real-world scenario where multimodal data from various categories are present.
In this paper, we present LPR4M, a large-scale multimodal dataset that covers
34 categories, comprises 3 modalities (image, video, and text), and is 50x
larger than the largest publicly available dataset. LPR4M contains diverse
videos and noise modality pairs while exhibiting a long-tailed distribution,
resembling real-world problems. Moreover, a cRoss-vIew semantiC alignmEnt
(RICE) model is proposed to learn discriminative instance features from the
image and video views of the products. This is achieved through instance-level
contrastive learning and cross-view patch-level feature propagation. A novel
Patch Feature Reconstruction loss is proposed to penalize the semantic
misalignment between cross-view patches. Extensive experiments demonstrate the
effectiveness of RICE and provide insights into the importance of dataset
diversity and expressivity. The dataset and code are available at
https://github.com/adxcreative/RICE
