LiveVQA: Live Visual Knowledge Seeking
We introduce LiveVQA, an automatically collected dataset of latest visual
knowledge from the Internet with synthesized VQA problems. LiveVQA consists of
3,602 single- and multi-hop visual questions from 6 news websites across 14
news categories, featuring high-quality image-text coherence and authentic
information. Our evaluation across 15 MLLMs (e.g., GPT-4o, Gemma-3, and
Qwen-2.5-VL family) demonstrates that stronger models perform better overall,
with advanced visual reasoning capabilities proving crucial for complex
multi-hop questions. Despite excellent performance on textual problems, models
with tools like search engines still show significant gaps when addressing
visual questions requiring latest visual knowledge, highlighting important
areas for future research.
67f4adf70864398533373364
Mingyang Fu
67f4adf70864398533373365
Yuyang Peng
admin_assigned
2025-04-08T10:35:30.731Z
67b945d34535b84d0b7223e8
https://cdn-avatars.huggingface.co/v1/production/uploads/67b945d34535b84d0b7223e8/iVviersiF1ijb1ut5LHEz.jpeg
Yuyang Peng
user
Ayiirep
67f4adf70864398533373366
Benlin Liu
admin_assigned
2025-04-08T10:34:51.813Z
6326723c21c98f83d6ad128e
/avatars/6c208ff815c037d0a6a2ada3198e7fd4.svg
Benlin Liu
user
Tim666
67f4adf70864398533373367
Yao Wan
67f4adf70864398533373368
Dongping Chen
admin_assigned
2025-04-08T10:34:42.707Z
643be8879f5d314db2d9ed23
https://cdn-avatars.huggingface.co/v1/production/uploads/643be8879f5d314db2d9ed23/VrW2UtJ7ppOnGIYjTWd7b.png
Chen Dongping
user
shuaishuaicdp
LiveVQA: Live Visual Knowledge Seeking
We introduce LiveVQA, an automatically collected dataset of latest visual
knowledge from the Internet with synthesized VQA problems. LiveVQA consists of
3,602 single- and multi-hop visual questions from 6 news websites across 14
news categories, featuring high-quality image-text coherence and authentic
information. Our evaluation across 15 MLLMs (e.g., GPT-4o, Gemma-3, and
Qwen-2.5-VL family) demonstrates that stronger models perform better overall,
with advanced visual reasoning capabilities proving crucial for complex
multi-hop questions. Despite excellent performance on textual problems, models
with tools like search engines still show significant gaps when addressing
visual questions requiring latest visual knowledge, highlighting important
areas for future research.
Evaluation of various MLLMs on LiveVQA, a dataset of visual questions with up-to-date visual knowledge, shows that advanced visual reasoning is essential for complex multi-hop questions.
MLLMs GPT-4o Gemma-3 Qwen-2.5-VL visual reasoning visual questions multi-hop questions visual knowledge