565 citations · 565 across the 5 of their papers we have counts for
5 papers
ARMADA: Attribute-Based Multimodal Data Augmentation
Xiaomeng Jin, Jeonghwan Kim, Yu Zhou +4
In Multimodal Language Models (MLMs), the cost of manually annotating high-quality image-text pair data for fine-tuning and alignment is extremely high. While existing multimodal d…
ACQUIRED: A Dataset for Answering Counterfactual Questions In Real-Life Videos
Te-Lin Wu, Zi-Yi Dou, Qingyuan Hu +5
Multimodal counterfactual reasoning is a vital yet challenging ability for AI systems. It involves predicting the outcomes of hypothetical circumstances based on vision and languag…
Localizing Active Objects from Egocentric Vision with Symbolic World Knowledge
Te-Lin Wu, Yu Zhou, Nanyun Peng
The ability to actively ground task instructions from an egocentric view is crucial for AI agents to accomplish tasks or assist humans virtually. One important step towards this go…
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning
Mingyu Derek Ma, Muhao Chen, Te-Lin Wu +1
Taxonomies are valuable resources for many applications, but the limited coverage due to the expensive manual curation process hinders their general applicability. Prior works atte…