5 papers
RetouchIQ: MLLM Agents for Instruction-Based Image Retouching with Generalist Reward
Qiucheng Wu, Jing Shi, Simon Jenni +4
Recent advances in multimodal large language models (MLLMs) have shown great potential for extending vision-language reasoning to professional tool-based image editing, enabling in…
More Than the Final Answer: Improving Visual Extraction and Logical Consistency in Vision-Language Models
Hoang Anh Just, Yifei Fan, Handong Zhao +6
Reinforcement learning from verifiable rewards (RLVR) has recently been extended from text-only LLMs to vision-language models (VLMs) to elicit long-chain multimodal reasoning. How…
The Photographer Eye: Teaching Multimodal Large Language Models to Understand Image Aesthetics like Photographers
Daiqing Qi, Handong Zhao, Jing Shi +5
While editing directly from life, photographers have found it too difficult to see simultaneously both the blue and the sky. Photographer and curator, Szarkowski insightfully revea…
Improving Large Vision and Language Models by Learning from a Panel of Peers
Jefferson Hernandez, Jing Shi, Simon Jenni +2
Traditional alignment methods for Large Vision and Language Models (LVLMs) primarily rely on human-curated preference data. Human-generated preference data is costly; machine-gener…
MAGNET: Augmenting Generative Decoders with Representation Learning and Infilling Capabilities
Savya Khosla, Aditi Tiwari, Kushal Kafle +4
While originally designed for unidirectional generative modeling, decoder-only large language models (LLMs) are increasingly being adapted for bidirectional modeling. However, unid…