Our Research and Publications on AI
Through our research, NetMind seeks to address the most critical challenges and opportunities in AI, and to create a brighter future for humanity by unlocking the potential of AGI.
Publication
A New Dataset and Method for Creativity Assessment Using the Alternate Uses Task
Luning Sun, Hongyi Gu, Rebecca Myers and Zheng Yuan
Transform-Equivariant Consistency Learning for Temporal Sentence Grounding
Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou, Zichuan Xu, Haozhao Wang, Xing Di, Weining Lu and Yu Cheng
ProS: Facial Omni-Representation Learning via Prototype-Based Self-Distillation
Xing Di, Yiyu Zheng, Xiaoming Liu and Yu Cheng
You Are Catching My Attention: Are Vision Transformers Bad Learners under Backdoor Attacks?
Zenghui Yuan, Pan Zhou, Kai Zou and Yu Cheng
Annotations Are Not All You Need: A Cross-modal Knowledge Transfer Network for Unsupervised Temporal Sentence Grounding
Xiang Fang, Daizong Liu, Wanlong Fang, Pan Zhou, Yu Cheng, Keke Tang and Kai Zou
Filling the Information Gap between Video and Query for Language-Driven Moment Retrieval
Daizong Liu, Xiaoye Qu, Jianfeng Dong, Guoshun Nan, Pan Zhou, Zichuan Xu, Lixing Chen, He Yan and Yu Cheng
Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks
Andrea Sottana, Bin Liang, Kai Zou and Zheng Yuan
Hypotheses Tree Building for One-Shot Temporal Sentence Localization.
Daizong Liu, Xiang Fang, Pan Zhou, Xing Di, Weining Lu and Yu Cheng.
Rethinking the Video Sampling and Reasoning Strategies for Temporal Sentence Grounding.
Jiahao Zhu, Daizong Liu, Pan Zhou, Xing Di, Yu Cheng, Song Yang, Wenzheng Xu, Zichuan Xu, Yao Wan, Lichao Sun and Zeyu Xiong.
M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design
Hanxue Liang, Zhiwen Fan, Rishov Sarkar, Ziyu Jiang, Tianlong Chen, Kai Zou, Yu Cheng, Cong Hao and Zhangyang Wang
Local Byte Fusion for Neural Machine Translation
Makesh Narsimhan Sreedhar, Xiangpeng Wan, Yu Cheng and Junjie Hu
RASAT: Integrating Relational Structures into Pretrained Seq2Seq Model for Text-to-SQL.
Jiexing Qi, Jingyao Tang, Ziwei He, Xiangpeng Wan, Yu Cheng, Chenghu Zhou, Xinbing Wang, Quanshi Zhang and Zhouhan Lin.
Backdoor Attacks on Crowd Counting.
Yuhua Sun, Tailai Zhang, Pan Zhou, Zichuan Xu, Xing Di and Yu Cheng.
Quantifying and alleviating political bias in language models
Yuhua Sun, Tailai Zhang, Pan Zhou, Zichuan Xu, Xing Di and Yu Cheng.
Unsupervised Temporal Video Grounding with Deep Semantic Clustering.
Daizong Liu, Xiaoye Qu, Yinzhen Wang, Xing Di, Kai Zou, Yu Cheng, Zichuan Xu and Pan Zhou
Memory-Guided Semantic Learning Network for Temporal Sentence Grounding.
Daizong Liu, Xiaoye Qu, Xing Di, Yu Cheng, Zichuan Xu and Pan Zhou
Few-shot text classification with triplet networks, data augmentation, and curriculum learning.
Jason Wei, Chengyu Huang, Soroush Vosoughi, Yu Cheng and Shiqi Xu.
Text augmentation in a multi-task view.
Jason Wei, Chengyu Huang, Shiqi Xu and Soroush Vosoughi.
Mitigating political bias in language models through reinforced calibration.
Ruibo Liu , Chenyan Jia, Jason Wei, Guangxuan Xu, Lili Wang and Soroush Vosoughi.
An empirical survey of unsupervised text representation methods on twitter data.
Lili Wang, Chongyang Gao, Jason Wei, Weicheng Ma, Ruibo Liu and Soroush Vosoughi.
What Are People Asking About COVID-19? A Question Classification Dataset
Jerry Wei, Chengyu Huang, Soroush Vosoughi and Jason Wei
EDA: Easy Data Augmentation techniques for boosting performance on text classification tasks.
Jason Wei and Kai Zou.
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