Dong Wang

Huimin Zeng, Zhenrui Yue, Dong Wang

Existing federated learning (FL) studies usually assume the training label space and test label space are identical. However, in real-world applications, this assumption is too ideal to be true. A new user could come up with queries that involve data from unseen classes, and such open-vocabulary queries would directly defect such FL systems. Therefore, in this work, we explicitly focus on the under-explored open-vocabulary challenge in FL. That is, for a new user, the global server shall understand her/his query that involves arbitrary unknown classes. To address this problem, we leverage the pre-trained vision-language models (VLMs). In particular, we present a novel adaptation framework tailored for VLMs in the context of FL, named as Federated Multimodal Prototyping (Fed-MP). Fed-MP adaptively aggregates the local model weights based on light-weight client residuals, and makes predictions based on a novel multimodal prototyping mechanism. Fed-MP exploits the knowledge learned from the seen classes, and robustifies the adapted VLM to unseen categories. Our empirical evaluation on various datasets validates the effectiveness of Fed-MP.

Haojun Yu, Youcheng Li, Zihan Niu et al.

Breast ultrasound (BUS) is an essential tool for diagnosing breast lesions, with millions of examinations per year. However, publicly available high-quality BUS benchmarks for AI development are limited in data scale and annotation richness. In this work, we present BUS-CoT, a BUS dataset for chain-of-thought (CoT) reasoning analysis, which contains 11,439 images of 10,019 lesions from 4,838 patients and covers all 99 histopathology types. To facilitate research on incentivizing CoT reasoning, we construct the reasoning processes based on observation, feature, diagnosis and pathology labels, annotated and verified by experienced experts. Moreover, by covering lesions of all histopathology types, we aim to facilitate robust AI systems in rare cases, which can be error-prone in clinical practice.

Jiyuan Zhang, Dong Wang

Research has shown that sequence-to-sequence neural models, particularly those with the attention mechanism, can successfully generate classical Chinese poems. However, neural models are not capable of generating poems that match specific styles, such as the impulsive style of Li Bai, a famous poet in the Tang Dynasty. This work proposes a memory-augmented neural model to enable the generation of style-specific poetry. The key idea is a memory structure that stores how poems with a desired style were generated by humans, and uses similar fragments to adjust the generation. We demonstrate that the proposed algorithm generates poems with flexible styles, including styles of a particular era and an individual poet.