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Found 8 papers, 2 papers with code
Ming-Omni: A Unified Multimodal Model for Perception and Generation
We propose Ming-Omni, a unified multimodal model capable of processing images, text, audio, and video, while demonstrating strong proficiency in both speech and image generation.
Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction
We introduce Ming-Lite-Uni, an open-source multimodal framework featuring a newly designed unified visual generator and a native multimodal autoregressive model tailored for unifying vision and language.
Skip-Vision: Efficient and Scalable Acceleration of Vision-Language Models via Adaptive Token Skipping
Transformer-based models have driven significant advancements in Multimodal Large Language Models (MLLMs), yet their computational costs surge drastically when scaling resolution, training data, and model parameters.
Accelerating Pre-training of Multimodal LLMs via Chain-of-Sight
This paper introduces Chain-of-Sight, a vision-language bridge module that accelerates the pre-training of Multimodal Large Language Models (MLLMs).
OrchMoE: Efficient Multi-Adapter Learning with Task-Skill Synergy
We advance the field of Parameter-Efficient Fine-Tuning (PEFT) with our novel multi-adapter method, OrchMoE, which capitalizes on modular skill architecture for enhanced forward transfer in neural networks.
Towards Better Vision-Inspired Vision-Language Models
In this paper we present a vision-inspired vision-language connection module dubbed as VIVL which efficiently exploits the vision cue for VL models.
Learning Implicit Entity-object Relations by Bidirectional Generative Alignment for Multimodal NER
Our BGA-MNER consists of \texttt{image2text} and \texttt{text2image} generation with respect to entity-salient content in two modalities.
Uncertainty-guided Learning for Improving Image Manipulation Detection
Image manipulation detection (IMD) is of vital importance as faking images and spreading misinformation can be malicious and harm our daily life.
