6 papers
Reprogramming Vision-Language Models via Structured Prompt Reparameterization
Zizhao Li, Chengyi Cai, Mohammed Yaqoob Ansari +3
Visual reprogramming adapts pretrained models to downstream tasks by modifying their input and output interfaces while keeping the backbone fixed. In vision-language models, existi…
You Only Reprogram Once: Rethinking Prolonged Training for Visual Reprogramming
Zizhao Li, Mohammed Yaqoob Ansari, Xinyu Su +3
Visual reprogramming is a parameter-efficient method for adapting pretrained models, yet its training can remain computationally expensive: even with a frozen backbone, visual prom…
Neural Distribution Prior for LiDAR Out-of-Distribution Detection
Zizhao Li, Zhengkang Xiang, Jiayang Ao +3
LiDAR-based perception is critical for autonomous driving due to its robustness to poor lighting and visibility conditions. Yet, current models operate under the closed-set assumpt…
Relative Energy Learning for LiDAR Out-of-Distribution Detection
Zizhao Li, Zhengkang Xiang, Jiayang Ao +2
Out-of-distribution (OOD) detection is a critical requirement for reliable autonomous driving, where safety depends on recognizing road obstacles and unexpected objects beyond the…
Out-of-distribution detection in 3D applications: a review
Zizhao Li, Xueyang Kang, Joseph West +1
The ability to detect objects that are not prevalent in the training set is a critical capability in many 3D applications, including autonomous driving. Machine learning methods fo…
From Open Vocabulary to Open World: Teaching Vision Language Models to Detect Novel Objects
Zizhao Li, Zhengkang Xiang, Joseph West +1
Traditional object detection methods operate under the closed-set assumption, where models can only detect a fixed number of objects predefined in the training set. Recent works on…