9 papers
VisualOverload: Probing Visual Understanding of VLMs in Really Dense Scenes
Paul Gavrikov, Wei Lin, M. Jehanzeb Mirza +6
Is basic visual understanding really solved in state-of-the-art VLMs? We present VisualOverload, a slightly different visual question answering (VQA) benchmark comprising 2,720 que…
PRISMM-Bench: A Benchmark of Peer-Review Grounded Multimodal Inconsistencies
Lukas Selch, Yufang Hou, M. Jehanzeb Mirza +4
Large Multimodal Models (LMMs) are increasingly applied to scientific research, yet it remains unclear whether they can reliably understand and reason over the multimodal complexit…
Feature Projection Learning for Better Vision-Language Reasoning
Yi Zhang, Weicheng Lin, Liang-Jie Zhang
Vision-Language Pre-Trained models, notably CLIP, that utilize contrastive learning have proven highly adept at extracting generalizable visual features. To inherit the well-learne…
TTRV: Test-Time Reinforcement Learning for Vision Language Models
Akshit Singh, Shyam Marjit, Wei Lin +7
Existing methods for extracting reward signals in Reinforcement Learning typically rely on labeled data and dedicated training splits, a setup that contrasts with how humans learn…
GLOV: Guided Large Language Models as Implicit Optimizers for Vision Language Models
M. Jehanzeb Mirza, Mengjie Zhao, Zhuoyuan Mao +12
In this work, we propose GLOV, which enables Large Language Models (LLMs) to act as implicit optimizers for Vision-Language Models (VLMs) to enhance downstream vision tasks. GLOV p…
Instructify: Demystifying Metadata to Visual Instruction Tuning Data Conversion
Jacob Hansen, Wei Lin, Junmo Kang +6
Visual Instruction Tuning (VisIT) data, commonly available as human-assistant conversations with images interleaved in the human turns, are currently the most widespread vehicle fo…