Tianyi Zhang

Tianyou Jiang, Mingshun Shao, Tianyi Zhang et al.

Automatic counting soybean pods and seeds in outdoor fields allows for rapid yield estimation before harvesting, while indoor laboratory counting offers greater accuracy. Both methods can significantly accelerate the breeding process. However, it remains challenging for accurately counting pods and seeds in outdoor fields, and there are still no accurate enough tools for counting pods and seeds in laboratories. In this study, we developed efficient deep learning models for counting soybean pods and seeds in both outdoor fields and indoor laboratories. For outdoor fields, annotating not only visible seeds but also occluded seeds makes YOLO have the ability to estimate the number of soybean seeds that are occluded. Moreover, we enhanced YOLO architecture by integrating it with HQ-SAM (YOLO-SAM), and domain adaptation techniques (YOLO-DA), to improve model robustness and generalization across soybean images taken in outdoor fields. Testing on soybean images from the outdoor field, we achieved a mean absolute error (MAE) of 6.13 for pod counting and 10.05 for seed counting. For the indoor setting, we utilized Mask-RCNN supplemented with a Swin Transformer module (Mask-RCNN-Swin), models were trained exclusively on synthetic training images generated from a small set of labeled data. This approach resulted in near-perfect accuracy, with an MAE of 1.07 for pod counting and 1.33 for seed counting across actual laboratory images from two distinct studies.

Jianyu Guan, Zongming Yin, Tianyi Zhang et al.

In recent years, the recommendation content on e-commerce platforms has become increasingly rich -- a single user feed may contain multiple entities, such as selling products, short videos, and content posts. To deal with the multi-entity recommendation problem, an intuitive solution is to adopt the shared-network-based architecture for joint training. The idea is to transfer the extracted knowledge from one type of entity (source entity) to another (target entity). However, different from the conventional same-entity cross-domain recommendation, multi-entity knowledge transfer encounters several important issues: (1) data distributions of the source entity and target entity are naturally different, making the shared-network-based joint training susceptible to the negative transfer issue, (2) more importantly, the corresponding feature schema of each entity is not exactly aligned (e.g., price is an essential feature for selling product while missing for content posts), making the existing methods no longer appropriate. Recent researchers have also experimented with the pre-training and fine-tuning paradigm. Again, they only consider the scenarios with the same entity type and feature systems, which is inappropriate in our case. To this end, we design a pre-training & fine-tuning based Multi-entity Knowledge Transfer framework called MKT. MKT utilizes a multi-entity pre-training module to extract transferable knowledge across different entities. In particular, a feature alignment module is first applied to scale and align different feature schemas. Afterward, a couple of knowledge extractors are employed to extract the common and entity-specific knowledge. In the end, the extracted common knowledge is adopted for target entity model training. Through extensive offline and online experiments, we demonstrated the superiority of MKT over multiple State-Of-The-Art methods.