7 citations · 15 across the 8 of their papers we have counts for
8 papers
InSpaceType: Reconsider Space Type in Indoor Monocular Depth Estimation
Cho-Ying Wu, Quankai Gao, Chin-Cheng Hsu +3
Indoor monocular depth estimation has attracted increasing research interest. Most previous works have been focusing on methodology, primarily experimenting with NYU-Depth-V2 (NYUv…
Character-Centric Story Visualization via Visual Planning and Token Alignment
Hong Chen, Rujun Han, Te-Lin Wu +2
Story visualization advances the traditional text-to-image generation by enabling multiple image generation based on a complete story. This task requires machines to 1) understand…
Learning Action Conditions from Instructional Manuals for Instruction Understanding
Te-Lin Wu, Caiqi Zhang, Qingyuan Hu +2
The ability to infer pre- and postconditions of an action is vital for comprehending complex instructions, and is essential for applications such as autonomous instruction-guided a…
Understanding Multimodal Procedural Knowledge by Sequencing Multimodal Instructional Manuals
Te-Lin Wu, Alex Spangher, Pegah Alipoormolabashi +3
The ability to sequence unordered events is an essential skill to comprehend and reason about real world task procedures, which often requires thorough understanding of temporal co…
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences
Shikhar Singh, Nuan Wen, Yu Hou +4
Commonsense reasoning is intuitive for humans but has been a long-term challenge for artificial intelligence (AI). Recent advancements in pretrained language models have shown prom…
LAMPRET: Layout-Aware Multimodal PreTraining for Document Understanding
Te-Lin Wu, Cheng Li, Mingyang Zhang +3
Document layout comprises both structural and visual (eg. font-sizes) information that is vital but often ignored by machine learning models. The few existing models which do use l…
MELINDA: A Multimodal Dataset for Biomedical Experiment Method Classification
Te-Lin Wu, Shikhar Singh, Sayan Paul +2
We introduce a new dataset, MELINDA, for Multimodal biomEdicaL experImeNt methoD clAssification. The dataset is collected in a fully automated distant supervision manner, where the…
Feedback Networks
Amir R. Zamir, Te-Lin Wu, Lin Sun +3
Currently, the most successful learning models in computer vision are based on learning successive representations followed by a decision layer. This is usually actualized through…