no code implementations • 24 Mar 2024 • Yunlong Tang, Daiki Shimada, Jing Bi, Chenliang Xu
In everyday communication, humans frequently use speech and gestures to refer to specific areas or objects, a process known as Referential Dialogue (RD).
1 code implementation • 27 Feb 2024 • Nguyen Nguyen, Jing Bi, Ali Vosoughi, Yapeng Tian, Pooyan Fazli, Chenliang Xu
To address these challenges, in this paper, we introduce the Object State Captioning and State Change Representation (OSCaR) dataset and benchmark.
1 code implementation • 29 Dec 2023 • Yunlong Tang, Jing Bi, Siting Xu, Luchuan Song, Susan Liang, Teng Wang, Daoan Zhang, Jie An, Jingyang Lin, Rongyi Zhu, Ali Vosoughi, Chao Huang, Zeliang Zhang, Feng Zheng, JianGuo Zhang, Ping Luo, Jiebo Luo, Chenliang Xu
With the burgeoning growth of online video platforms and the escalating volume of video content, the demand for proficient video understanding tools has intensified markedly.
1 code implementation • 18 Oct 2023 • Jing Bi, Nguyen Manh Nguyen, Ali Vosoughi, Chenliang Xu
Augmented reality (AR) requires the seamless integration of visual, auditory, and linguistic channels for optimized human-computer interaction.
1 code implementation • 15 Jun 2023 • Rahil Mehrizi, Arash Mehrjou, Maryana Alegro, Yi Zhao, Benedetta Carbone, Carl Fishwick, Johanna Vappiani, Jing Bi, Siobhan Sanford, Hakan Keles, Marcus Bantscheff, Cuong Nguyen, Patrick Schwab
High-content cellular imaging, transcriptomics, and proteomics data provide rich and complementary views on the molecular layers of biology that influence cellular states and function.
no code implementations • 9 Oct 2022 • Jing Bi, Vorapong Suppakitpaisarn
This study explores the robustness of learning by symmetric loss on private data.
no code implementations • ICCV 2021 • Jing Bi, Jiebo Luo, Chenliang Xu
In this work, we leverage instructional videos to study humans' decision-making processes, focusing on learning a model to plan goal-directed actions in real-life videos.
no code implementations • 29 Sep 2021 • Samuel Lerman, Jing Bi, Chenliang Xu
rQdia (pronounced “Arcadia”) regularizes Q-value distributions with augmented images in pixel-based deep reinforcement learning.
no code implementations • 3 Oct 2020 • Jing Shi, Jing Bi, Yingru Liu, Chenliang Xu
The marriage of recurrent neural networks and neural ordinary differential networks (ODE-RNN) is effective in modeling irregularly-observed sequences.
no code implementations • 4 Dec 2019 • Jing Bi, Vikas Dhiman, Tianyou Xiao, Chenliang Xu
The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer.
no code implementations • 1 Nov 2018 • Jing Bi, Tianyou Xiao, Qiuyue Sun, Chenliang Xu
Deep neural networks trained on demonstrations of human actions give robot the ability to perform self-driving on the road.