With the rapid advancement of e-commerce, exploring general representations rather than task-specific ones has attracted increasing research attention. For product understanding, although existing discriminative dual-flow architectures drive progress in this field, they inherently struggle to model the many-to-one alignment between multiple images and texts of products. Therefore, we argue that generative Multimodal Large Language Models (MLLMs) hold significant potential for improving product representation learning. Nevertheless, achieving this goal still remains non-trivial due to several key challenges: the lack of multimodal and aspect-aware modeling modules in typical LLMs; the common presence of background noise in product images; and the absence of a standard benchmark for evaluation. To address these issues, we propose the first generative MLLM-based model named MOON for product representation learning. Our method (1) employs a guided Mixture-of-Experts (MoE) module for targeted modeling of multimodal and aspect-specific product content; (2) effectively detects core semantic regions in product images to mitigate the distraction and interference caused by background noise; and (3) introduces the specialized negative sampling strategy to increase the difficulty and diversity of negative samples. In addition, we release a large-scale multimodal benchmark MBE for various product understanding tasks. Experimentally, our model demonstrates competitive zero-shot performance on both our benchmark and the public dataset, showcasing strong generalization across various downstream tasks, including cross-modal retrieval, product classification, and attribute prediction. Furthermore, the case study and visualization illustrate the effectiveness of MOON for product understanding.

Per-pixel masks of semantic objects are very useful in many applications, which, however, are tedious to be annotated. In this paper, we propose a collaborative annotation approach to efficiently generate per-pixel masks of semantic objects in tagged images with multi-granularity supervisions. Given a set of tagged images, a computer agent is dynamically generated to roughly localize the semantic objects described by the tag. The agent first extracts massive object proposals and then infer the tag-related ones under the weak and strong supervisions from linguistically and visually similar images as well as previously annotated objects. By representing such supervisions by over-complete dictionaries, tag-related proposals can pop-out according to their sparse coding length, which are then converted to superpixels with binary labels. After that, human annotators participate in the annotation by flipping labels and dividing superpixels with clicks, which are used as click supervisions that teaches the agent to recover false positives/negatives in processing images with the same tags. Experimental results show that our approach can facilitate the annotation and generate object masks that are consistent with those generated by the LabelMe toolbox.