153 citations · 186 across the 11 of their papers we have counts for
9 papers · 1 filter
Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters
Hongyu Zhao, Hao Tan, Hongyuan Mei
Adapter-tuning is a paradigm that transfers a pretrained language model to downstream tasks by adding and tuning a small number of new parameters. Previously proposed adapter archi…
VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer
Zineng Tang, Jaemin Cho, Hao Tan +1
Since visual perception can give rich information beyond text descriptions for world understanding, there has been increasing interest in leveraging visual grounding for language l…
Diagnosing the Environment Bias in Vision-and-Language Navigation
Yubo Zhang, Hao Tan, Mohit Bansal
Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-b…
The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions
Xiang Zhou, Yixin Nie, Hao Tan +1
We find that the performance of state-of-the-art models on Natural Language Inference (NLI) and Reading Comprehension (RC) analysis/stress sets can be highly unstable. This raises…
Modality-Balanced Models for Visual Dialogue
Hyounghun Kim, Hao Tan, Mohit Bansal
The Visual Dialog task requires a model to exploit both image and conversational context information to generate the next response to the dialogue. However, via manual analysis, we…
Expressing Visual Relationships via Language
Hao Tan, Franck Dernoncourt, Zhe Lin +2
Describing images with text is a fundamental problem in vision-language research. Current studies in this domain mostly focus on single image captioning. However, in various real a…