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VGent: Visual Grounding via Modular Design for Disentangling Reasoning and Prediction
Weitai Kang, Jason Kuen, Mengwei Ren +3
Current visual grounding models are either based on a Multimodal Large Language Model (MLLM) that performs auto-regressive decoding, which is slow and risks hallucinations, or on r…
Sparse-LaViDa: Sparse Multimodal Discrete Diffusion Language Models
Shufan Li, Jiuxiang Gu, Kangning Liu +4
Masked Discrete Diffusion Models (MDMs) have achieved strong performance across a wide range of multimodal tasks, including image understanding, generation, and editing. However, t…
Agentic Lybic: Multi-Agent Execution System with Tiered Reasoning and Orchestration
Liangxuan Guo, Bin Zhu, Qingqian Tao +5
Autonomous agents for desktop automation struggle with complex multi-step tasks due to poor coordination and inadequate quality control. We introduce Agentic Lybic, a novel multi-a…
Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation
Shufan Li, Jiuxiang Gu, Kangning Liu +4
We propose Lavida-O, a unified Masked Diffusion Model (MDM) for multimodal understanding and generation. Unlike existing multimodal MDMs such as MMaDa and Muddit which only support…
Refer to Any Segmentation Mask Group With Vision-Language Prompts
Shengcao Cao, Zijun Wei, Jason Kuen +6
Recent image segmentation models have advanced to segment images into high-quality masks for visual entities, and yet they cannot provide comprehensive semantic understanding for c…