M2-omni: Advancing Omni-MLLM for Comprehensive Modality Support with Competitive Performance
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:3Q3HSJ5Urecord.jsonopen to challenge →
read the original abstract
We present M2-omni, a cutting-edge, open-source omni-MLLM that achieves competitive performance to GPT-4o. M2-omni employs a unified multimodal sequence modeling framework, which empowers Large Language Models(LLMs) to acquire comprehensive cross-modal understanding and generation capabilities. Specifically, M2-omni can process arbitrary combinations of audio, video, image, and text modalities as input, generating multimodal sequences interleaving with audio, image, or text outputs, thereby enabling an advanced and interactive real-time experience. The training of such an omni-MLLM is challenged by significant disparities in data quantity and convergence rates across modalities. To address these challenges, we propose a step balance strategy during pre-training to handle the quantity disparities in modality-specific data. Additionally, a dynamically adaptive balance strategy is introduced during the instruction tuning stage to synchronize the modality-wise training progress, ensuring optimal convergence. Notably, we prioritize preserving strong performance on pure text tasks to maintain the robustness of M2-omni's language understanding capability throughout the training process. To our best knowledge, M2-omni is currently a very competitive open-source model to GPT-4o, characterized by its comprehensive modality and task support, as well as its exceptional performance. We expect M2-omni will advance the development of omni-MLLMs, thus facilitating future research in this domain.
This paper has not been read by Pith yet.
Forward citations
Cited by 7 Pith papers
-
TraceAV-Bench: Benchmarking Multi-Hop Trajectory Reasoning over Long Audio-Visual Videos
TraceAV-Bench is the first benchmark for multi-hop trajectory reasoning over long audio-visual videos, showing top models reach only 51-68% accuracy with substantial room for improvement.
-
Cross-Modal Coreference Alignment: Enabling Reliable Information Transfer in Omni-LLMs
Omni-LLMs show systematic failures at cross-modal coreference; a new dataset and both training-free and training-based fixes produce substantial gains on 13 models.
-
Omni-Perception Policy Optimization for Multimodal Emotion Reasoning
OPPO applies RL with an Omni-Perception Reward and masked-input KL loss to boost cue utilization and suppress hallucinations in emotion reasoning MLLMs, claiming SOTA results on MER-UniBench, MME-Emotion, and MEP-Bench.
-
Pareto LoRA: Mitigating Modality Imbalance in Unified Multimodal Models via Pareto-Optimal Gradient Integration
Pareto LoRA applies Pareto-optimal gradient integration to balance text and image objectives in LoRA-based fine-tuning of unified multimodal models, reporting up to 44.9% gains in image quality on the CoMM benchmark w...
-
Training-Free Multimodal Large Language Model Orchestration
LLM Orchestration integrates modality experts via an LLM controller, cross-modal memory, and interaction layer to enable multimodal input-output without gradient-based training.
-
Towards Understanding Modality Interaction in Multimodal Language Models via Partial Information Decomposition
PID applied to MLLMs identifies task-specific modality interaction profiles that generalize across models, extend to tri-modal cases, and yield initial performance gains via reweighting.
-
Training-Free Multimodal Large Language Model Orchestration
A training-free orchestration framework integrates off-the-shelf modality experts via an LLM controller, text-centric cross-modal memory, and unified interaction layer to enable multimodal input-output without joint training.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.