no code implementations • 18 May 2023 • Wanrong Zhu, Xinyi Wang, Yujie Lu, Tsu-Jui Fu, Xin Eric Wang, Miguel Eckstein, William Yang Wang
We conduct a series of experiments to compare the common edits made by humans and GPT-k, evaluate the performance of GPT-k in prompting T2I, and examine factors that may influence this process.
1 code implementation • 7 Oct 2022 • Wanrong Zhu, An Yan, Yujie Lu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, William Yang Wang
Recent advances in text-to-image synthesis make it possible to visualize machine imaginations for a given context.
no code implementations • 6 Jun 2022 • Yujie Lu, Weixi Feng, Wanrong Zhu, Wenda Xu, Xin Eric Wang, Miguel Eckstein, William Yang Wang
Procedural planning aims to implement complex high-level goals by decomposition into sequential simpler low-level steps.
1 code implementation • NAACL 2022 • Yujie Lu, Wanrong Zhu, Xin Eric Wang, Miguel Eckstein, William Yang Wang
Human brains integrate linguistic and perceptual information simultaneously to understand natural language, and hold the critical ability to render imaginations.
no code implementations • 10 Jun 2021 • Wanrong Zhu, Xin Eric Wang, An Yan, Miguel Eckstein, William Yang Wang
Automatic evaluations for natural language generation (NLG) conventionally rely on token-level or embedding-level comparisons with text references.
1 code implementation • NAACL 2022 • Wanrong Zhu, Yuankai Qi, Pradyumna Narayana, Kazoo Sone, Sugato Basu, Xin Eric Wang, Qi Wu, Miguel Eckstein, William Yang Wang
Results show that indoor navigation agents refer to both object and direction tokens when making decisions.
1 code implementation • EMNLP 2020 • Tsu-Jui Fu, Xin Eric Wang, Scott Grafton, Miguel Eckstein, William Yang Wang
In this paper, we introduce a Self-Supervised Counterfactual Reasoning (SSCR) framework that incorporates counterfactual thinking to overcome data scarcity.
no code implementations • 17 Nov 2019 • Tsu-Jui Fu, Xin Eric Wang, Matthew Peterson, Scott Grafton, Miguel Eckstein, William Yang Wang
In particular, we present a model-agnostic adversarial path sampler (APS) that learns to sample challenging paths that force the navigator to improve based on the navigation performance.
1 code implementation • ICLR 2019 • Arturo Deza, Aditya Jonnalagadda, Miguel Eckstein
The problem of $\textit{visual metamerism}$ is defined as finding a family of perceptually indistinguishable, yet physically different images.
no code implementations • CVPR 2015 • Karthikeyan Shanmuga Vadivel, Thuyen Ngo, Miguel Eckstein, B. S. Manjunath
The proposed algorithm extracts dominant visual tracks using eye tracking data from multiple subjects on a video sequence by a combination of mean-shift clustering and Hungarian algorithm.