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20212023
most citedHow Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?

30 citations · 46 across the 9 of their papers we have counts for

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6 papers · 1 filter

cs.CV20239 cited

Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection

Kaifeng Gao, Long Chen, Hanwang Zhang +2

Prompt tuning with large-scale pretrained vision-language models empowers open-vocabulary predictions trained on limited base categories, e.g., object classification and detection.…

cs.CV2022

Equivariance and Invariance Inductive Bias for Learning from Insufficient Data

Tan Wang, Qianru Sun, Sugiri Pranata +2

We are interested in learning robust models from insufficient data, without the need for any externally pre-trained checkpoints. First, compared to sufficient data, we show why ins…

cs.CV20223 cited

On Non-Random Missing Labels in Semi-Supervised Learning

Xinting Hu, Yulei Niu, Chunyan Miao +2

Semi-Supervised Learning (SSL) is fundamentally a missing label problem, in which the label Missing Not At Random (MNAR) problem is more realistic and challenging, compared to the…

cs.CV20221 cited

RoME: Role-aware Mixture-of-Expert Transformer for Text-to-Video Retrieval

Burak Satar, Hongyuan Zhu, Hanwang Zhang +1

Seas of videos are uploaded daily with the popularity of social channels; thus, retrieving the most related video contents with user textual queries plays a more crucial role. Most…

cs.CV2021

Deconfounded Visual Grounding

Jianqiang Huang, Yu Qin, Jiaxin Qi +2

We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck. For example,…

cs.CV2021

Cross-Domain Empirical Risk Minimization for Unbiased Long-tailed Classification

Beier Zhu, Yulei Niu, Xian-Sheng Hua +1

We address the overlooked unbiasedness in existing long-tailed classification methods: we find that their overall improvement is mostly attributed to the biased preference of tail…