11 papers
PET-F2I: A Comprehensive Benchmark and Parameter-Efficient Fine-Tuning of LLMs for PET/CT Report Impression Generation
Yuchen Liu, Wenbo Zhang, Liling Peng +6
PET/CT imaging is pivotal in oncology and nuclear medicine, yet summarizing complex findings into precise diagnostic impressions is labor-intensive. While LLMs have shown promise i…
Future Optical Flow Prediction Improves Robot Control & Video Generation
Kanchana Ranasinghe, Honglu Zhou, Yu Fang +7
Future motion representations, such as optical flow, offer immense value for control and generative tasks. However, forecasting generalizable spatially dense motion representations…
Robotic VLA Benefits from Joint Learning with Motion Image Diffusion
Yu Fang, Kanchana Ranasinghe, Le Xue +10
Vision-Language-Action (VLA) models have achieved remarkable progress in robotic manipulation by mapping multimodal observations and instructions directly to actions. However, they…
BLIP3o-NEXT: Next Frontier of Native Image Generation
Jiuhai Chen, Le Xue, Zhiyang Xu +12
We present BLIP3o-NEXT, a fully open-source foundation model in the BLIP3 series that advances the next frontier of native image generation. BLIP3o-NEXT unifies text-to-image gener…
xGen-MM (BLIP-3): A Family of Open Large Multimodal Models
Le Xue, Manli Shu, Anas Awadalla +30
This paper introduces BLIP-3, an open framework for developing Large Multimodal Models (LMMs). The framework comprises meticulously curated datasets, a training recipe, model archi…
Contra4: Evaluating Contrastive Cross-Modal Reasoning in Audio, Video, Image, and 3D
Artemis Panagopoulou, Le Xue, Honglu Zhou +6
Real-world decision-making often begins with identifying which modality contains the most relevant information for a given query. While recent multimodal models have made impressiv…