I am Xin Wang, a Ph.D. student at UC Berkeley, working with
Prof. Trevor Darrell and Prof. Joseph E. Gonzalez.
I am part of the BAIR Lab,
RISE Lab, and BDD Lab.
Prior to UC Berkeley, I obtained my B.S. degree in Computer Science from Shanghai Jiao Tong University .
My research interest lies at the intersection of computer vision, machine learning and learning systems. I take a holistic view of intelligent systems, aiming to build vision systems that are adaptive, continual, efficient and can operate in real-time. I describe the premise of next-generation visual intelligent systems as solving the “last-mile delivery” problem of computer vision, which requires models capable of adapting to the challenging application scenarios with limited data, annotation, and computation.
As a Computer Vision researcher with a computer system emphasis, I work on a full stack solution to
build visual intelligent systems from data collection and annotation,
algorithmic designs of adaptive models, to real-time model serving.
For model designs, I pioneer dynamic neural networks where the
network parameters and structures are dynamically determined by various test-time information, such as the input image itself
or task descriptions. Such test-time adaptation approach bridges the gap between training and inference, allowing the models to continuously
learn and adapt to changing environments. For example, I propose SkipNet,
where the execution paths are dynamically decided on a per input basis to reduce inference cost while maintaining the full model capacity and accuracy.
Some other examples are constructing task-aware feature embeddings
through parameter generation and test-time fine-tuning for few-shot image classification and object detection.
I also explore various paradigms for learning with fewer data and labels during training, such as continual learning, heterogenous multi-task learning and weakly/semi-supervised learning.