Xu Luo

I am a Ph.D. candidate at UESTC, advised by Prof. Jingkuan Song. My research aims to bridge the gap between virtual AI and the physical world by developing general-purpose robots, focusing on the creation of generalist policies that empower them to robustly perceive, dynamically interact, and continuously adapt—ultimately enabling them to tackle any task, in any environment.

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Identifying shortcut learning as a key impediment to the generalization of generalist robot policies and providing a comprehensive analysis.

Text-to-any-modality models that generate images, videos, audio, and 3D multiview images conditioned on text in a flow-based diffusion framework, using novel Flag-DiT architectures with up to 5B parameters and 128K context windows.

Uncovering and analyzing extreme feature redundancy phenomenon of pretrained vision models when transferring to few-shot tasks.

Proposing DETA--a framework that solves potential data/label noise in downstream few-shot transfer tasks.

Empirically proving the disentanglement of training and adaptation algorithms in few-shot classification, and performing interesting analysis of each phase that leads to the discovery of several important observations.

Revealing a strong bias caused by the centroid of features in each few-shot learning task. A simple method is designed to rectify this bias by removing the dimension along the direction of task centroid from the feature space.

Revealing and analyzing the channel bias problem that we found critical in few-shot learning, through a simple channel-wise feature transformation applied only at test time.

Identifying image background as a shortcut knowledge ungeneralizable beyond training categories in Few-Shot Learning. A novel framework, COSOC, is designed to tackle this problem.

Applying contrastive learning to Few-Shot Learning, with views generated in a learning-to-learn fashion.

This well-designed template is borrowed from this guy