51 citations · 95 across the 13 of their papers we have counts for
13 papers
mmWalk: Towards Multi-modal Multi-view Walking Assistance
Kedi Ying, Ruiping Liu, Chongyan Chen +5
Walking assistance in extreme or complex environments remains a significant challenge for people with blindness or low vision (BLV), largely due to the lack of a holistic scene und…
Acknowledging Focus Ambiguity in Visual Questions
Chongyan Chen, Yu-Yun Tseng, Zhuoheng Li +2
No published work on visual question answering (VQA) accounts for ambiguity regarding where the content described in the question is located in the image. To fill this gap, we intr…
Long-Form Answers to Visual Questions from Blind and Low Vision People
Mina Huh, Fangyuan Xu, Yi-Hao Peng +5
Vision language models can now generate long-form answers to questions about images - long-form visual question answers (LFVQA). We contribute VizWiz-LF, a dataset of long-form ans…
An Evaluation of GPT-4V and Gemini in Online VQA
Mengchen Liu, Chongyan Chen, Danna Gurari
While there is much excitement about the potential of large multimodal models (LMM), a comprehensive evaluation is critical to establish their true capabilities and limitations. In…
Fully Authentic Visual Question Answering Dataset from Online Communities
Chongyan Chen, Mengchen Liu, Noel Codella +3
Visual Question Answering (VQA) entails answering questions about images. We introduce the first VQA dataset in which all contents originate from an authentic use case. Sourced fro…
VQA Therapy: Exploring Answer Differences by Visually Grounding Answers
Chongyan Chen, Samreen Anjum, Danna Gurari
Visual question answering is a task of predicting the answer to a question about an image. Given that different people can provide different answers to a visual question, we aim to…