2 citations · 4 across the 17 of their papers we have counts for
6 papers · 1 filter
Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget
Guoxuan Chen, Chufeng Xiao, Haoran Yang +30
We introduce Boogu-Image-0.1, an open-source unified multimodal understanding and generation model family, comprising Base, Turbo, Edit, and Edit-Turbo variants. It delivers compet…
Thinking Without Images: Internalizing Visual Manipulation with On-Policy Self-Distillation
Yishuo Cai, Jiahui Liu, Yuanxin Liu +9
''Thinking with Images'' has emerged as an effective paradigm for fine-grained visual reasoning: by explicitly zooming into relevant regions and reasoning over crops, models can ac…
InSight-o3: Empowering Multimodal Foundation Models with Generalized Visual Search
Kaican Li, Lewei Yao, Jiannan Wu +7
The ability for AI agents to "think with images" requires a sophisticated blend of reasoning and perception. However, current open multimodal agents still largely fall short on the…
Fourier Compressor: Frequency-Domain Visual Token Compression for Vision-Language Models
Huanyu Wang, Jushi Kai, Haoli Bai +4
Vision-Language Models (VLMs) incur substantial computational overhead and inference latency due to the large number of vision tokens introduced by high-resolution image and video…
EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions
Kai Chen, Yunhao Gou, Runhui Huang +28
GPT-4o, an omni-modal model that enables vocal conversations with diverse emotions and tones, marks a milestone for omni-modal foundation models. However, empowering Large Language…
MoPE-CLIP: Structured Pruning for Efficient Vision-Language Models with Module-wise Pruning Error Metric
Haokun Lin, Haoli Bai, Zhili Liu +5
Vision-language pre-trained models have achieved impressive performance on various downstream tasks. However, their large model sizes hinder their utilization on platforms with lim…