Jiancheng Wang

Unreliable pseudo-labels in object detection can be transformed into reliable training signals, leading to substantial performance gains across domains.

DNNs in recommendation models don't just learn feature interactions, they fundamentally reshape embedding spaces by preventing dimensional collapse.

VLMs in self-driving cars are shockingly vulnerable: a subtle combination of graffiti and foreign-language commands can hijack their behavior without degrading performance on normal tasks.