Teng Xu

Can Karakus, Rahul Huilgol, Fei Wu et al.

With deep learning models rapidly growing in size, systems-level solutions for large-model training are required. We present Amazon SageMaker model parallelism, a software library that integrates with PyTorch, and enables easy training of large models using model parallelism and other memory-saving features. In contrast to existing solutions, the implementation of the SageMaker library is much more generic and flexible, in that it can automatically partition and run pipeline parallelism over arbitrary model architectures with minimal code change, and also offers a general and extensible framework for tensor parallelism, which supports a wider range of use cases, and is modular enough to be easily applied to new training scripts. The library also preserves the native PyTorch user experience to a much larger degree, supporting module re-use and dynamic graphs, while giving the user full control over the details of the training step. We evaluate performance over GPT-3, RoBERTa, BERT, and neural collaborative filtering, and demonstrate competitive performance over existing solutions.

Youjia Wang, Taotao Zhou, Minzhang Li et al.

Photo-realistic video portrait reenactment benefits virtual production and numerous VR/AR experiences. The task remains challenging as the reenacted expression should match the source while the lighting should be adjustable to new environments. We present a neural relighting and expression transfer technique to transfer the facial expressions from a source performer to a portrait video of a target performer while enabling dynamic relighting. Our approach employs 4D reflectance field learning, model-based facial performance capture and target-aware neural rendering. Specifically, given a short sequence of the target performer's OLAT, we apply a rendering-to-video translation network to first synthesize the OLAT result of new sequences with unseen expressions. We then design a semantic-aware facial normalization scheme along with a multi-frame multi-task learning strategy to encode the content, segmentation, and motion flows for reliably inferring the reflectance field. This allows us to simultaneously control facial expression and apply virtual relighting. Extensive experiments demonstrate that our technique can robustly handle challenging expressions and lighting environments and produce results at a cinematographic quality.

Shuang Peng, Mengdi Zhou, Minghui Yang et al.

In the Chinese medical insurance industry, the assessor's role is essential and requires significant efforts to converse with the claimant. This is a highly professional job that involves many parts, such as identifying personal information, collecting related evidence, and making a final insurance report. Due to the coronavirus (COVID-19) pandemic, the previous offline insurance assessment has to be conducted online. However, for the junior assessor often lacking practical experience, it is not easy to quickly handle such a complex online procedure, yet this is important as the insurance company needs to decide how much compensation the claimant should receive based on the assessor's feedback. In order to promote assessors' work efficiency and speed up the overall procedure, in this paper, we propose a dialogue-based information extraction system that integrates advanced NLP technologies for medical insurance assessment. With the assistance of our system, the average time cost of the procedure is reduced from 55 minutes to 35 minutes, and the total human resources cost is saved 30% compared with the previous offline procedure. Until now, the system has already served thousands of online claim cases.