27 citations · 50 across the 21 of their papers we have counts for
5 papers · 1 filter
ProVision: Programmatically Scaling Vision-centric Instruction Data for Multimodal Language Models
Jieyu Zhang, Le Xue, Linxin Song +11
With the rise of multimodal applications, instruction data has become critical for training multimodal language models capable of understanding complex image-based queries. Existin…
LATTE: Learning to Think with Vision Specialists
Zixian Ma, Jianguo Zhang, Zhiwei Liu +9
While open-source vision-language models perform well on simple question-answering, they still struggle with complex questions that require both perceptual and reasoning capabiliti…
NaturalBench: Evaluating Vision-Language Models on Natural Adversarial Samples
Baiqi Li, Zhiqiu Lin, Wenxuan Peng +7
Vision-language models (VLMs) have made significant progress in recent visual-question-answering (VQA) benchmarks that evaluate complex visio-linguistic reasoning. However, are the…
Task Me Anything
Jieyu Zhang, Weikai Huang, Zixian Ma +7
Benchmarks for large multimodal language models (MLMs) now serve to simultaneously assess the general capabilities of models instead of evaluating for a specific capability. As a r…
m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks
Zixian Ma, Weikai Huang, Jieyu Zhang +2
Real-world multi-modal problems are rarely solved by a single machine learning model, and often require multi-step computational plans that involve stitching several models. Tool-a…