1 code implementation • ECCV 2020 • Youngjoong Kwon, Stefano Petrangeli, Dahun Kim, Haoliang Wang, Eunbyung Park, Viswanathan Swaminathan, Henry Fuchs
Second, we introduce a novel loss to explicitly enforce consistency across generated views both in space and in time.
no code implementations • 14 Mar 2024 • Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, YuHang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang
Causal inference has shown potential in enhancing the predictive accuracy, fairness, robustness, and explainability of Natural Language Processing (NLP) models by capturing causal relationships among variables.
no code implementations • 4 Dec 2023 • Yizhou Wang, Ruiyi Zhang, Haoliang Wang, Uttaran Bhattacharya, Yun Fu, Gang Wu
Recent advancements in language-model-based video understanding have been progressing at a remarkable pace, spurred by the introduction of Large Language Models (LLMs).
no code implementations • 23 Nov 2023 • Chen Zhao, Kai Jiang, Xintao Wu, Haoliang Wang, Latifur Khan, Christan Grant, Feng Chen
Achieving the generalization of an invariant classifier from source domains to shifted target domains while simultaneously considering model fairness is a substantial and complex challenge in machine learning.
no code implementations • 18 Sep 2023 • Haoliang Wang, Chen Zhao, Yunhui Guo, Kai Jiang, Feng Chen
In this study, we introduce a novel problem, semantic OOD detection across domains, which simultaneously addresses both distributional shifts.
no code implementations • 22 May 2023 • Julio Martinez, Felix Binder, Haoliang Wang, Nick Haber, Judith Fan, Daniel L. K. Yamins
Finally, linearly combining the adversarial model with the number of collisions in a scene leads to the greatest improvement in predictivity of human responses, suggesting humans are driven towards scenarios that result in high information gain and physical activity.
no code implementations • 20 May 2023 • Kaige Xie, Tong Yu, Haoliang Wang, Junda Wu, Handong Zhao, Ruiyi Zhang, Kanak Mahadik, Ani Nenkova, Mark Riedl
In this paper, we focus on improving the prompt transfer from dialogue state tracking to dialogue summarization and propose Skeleton-Assisted Prompt Transfer (SAPT), which leverages skeleton generation as extra supervision that functions as a medium connecting the distinct source and target task and resulting in the model's better consumption of dialogue state information.
1 code implementation • 1 Mar 2022 • Haoliang Wang, Chen Zhao, Xujiang Zhao, Feng Chen
During the forward pass of Deep Neural Networks (DNNs), inputs gradually transformed from low-level features to high-level conceptual labels.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 29 Sep 2021 • Mustafa Abdallah, Ryan Rossi, Kanak Mahadik, Sungchul Kim, Handong Zhao, Haoliang Wang, Saurabh Bagchi
In this work, we develop techniques for fast automatic selection of the best forecasting model for a new unseen time-series dataset, without having to first train (or evaluate) all the models on the new time-series data to select the best one.
1 code implementation • 30 Jun 2021 • William P. McCarthy, Robert D. Hawkins, Haoliang Wang, Cameron Holdaway, Judith E. Fan
Many real-world tasks require agents to coordinate their behavior to achieve shared goals.