2 code implementations • 28 May 2022 • Tian Lv, Chongyang Bai, Chaojie Wang
To resolve it, we present (i) multi-dimensional MLP (MDMLP), a conceptually simple and lightweight MLP-based architecture yet achieves SOTA when training from scratch on small-size datasets; (ii) multi-dimension MLP Attention Tool (MDAttnTool), a novel and efficient attention mechanism based on MLPs.
1 code implementation • 8 Dec 2021 • Wenbin Teng, Chongyang Bai
In this work, we propose a Multimodal Training Unimodal Test (MTUT) framework for robust face classification, which exploits the cross-modality relationship during training and applies it as a complementary of the imperfect single modality input during testing.
1 code implementation • 29 Jul 2021 • Chongyang Bai, Xiaoxue Zang, Ying Xu, Srinivas Sunkara, Abhinav Rastogi, Jindong Chen, Blaise Aguera y Arcas
Our key intuition is that the heterogeneous features in a UI are self-aligned, i. e., the image and text features of UI components, are predictive of each other.
no code implementations • 3 Jun 2020 • Chongyang Bai, Haipeng Chen, Srijan Kumar, Jure Leskovec, V. S. Subrahmanian
Our M2P2 (Multimodal Persuasion Prediction) framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem.
no code implementations • 3 Jun 2019 • Chongyang Bai, Tommy White, Linda Xiao, V. S. Subrahmanian, Ziheng Zhou
Moreover, we experimentally show that the use of similarity metrics within our C2P2 algorithm leads to a direct improvement for 20 out of 21 cryptocurrencies ranging from 0. 4% to 17. 8%.
no code implementations • 15 May 2019 • Chongyang Bai, Maksim Bolonkin, Judee Burgoon, Chao Chen, Norah Dunbar, Bharat Singh, V. S. Subrahmanian, Zhe Wu
Most work on automated deception detection (ADD) in video has two restrictions: (i) it focuses on a video of one person, and (ii) it focuses on a single act of deception in a one or two minute video.