Wei Gao

Shuqing Luo, Bowen Qu, Wei Gao

In this paper, we explore a critical yet under-investigated issue: how to learn robust and well-generalized 3D representation from pre-trained vision language models such as CLIP. Previous works have demonstrated that cross-modal distillation can provide rich and useful knowledge for 3D data. However, like most deep learning models, the resultant 3D learning network is still vulnerable to adversarial attacks especially the iterative attack. In this work, we propose Dual Denoising, a novel framework for learning robust and well-generalized 3D representations from CLIP. It combines a denoising-based proxy task with a novel feature denoising network for 3D pre-training. Additionally, we propose utilizing parallel noise inference to enhance the generalization of point cloud features under cross domain settings. Experiments show that our model can effectively improve the representation learning performance and adversarial robustness of the 3D learning network under zero-shot settings without adversarial training. Our code is available at https://github.com/luoshuqing2001/Dual_Denoising.

Fengzhu Zeng, Wei Gao

Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored by fact-checkers. Therefore, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim for \underline{Ex}plainable fact-checking of real-world \underline{Claim}s, and introduce JustiLM, a novel few-shot \underline{Justi}fication generation based on retrieval-augmented \underline{L}anguage \underline{M}odel by using fact-check articles as auxiliary resource during training only. Experiments show that JustiLM achieves promising performance in justification generation compared to strong baselines, and can also enhance veracity classification with a straightforward extension.