OrdMoE: Preference Alignment via Hierarchical Expert Group Ranking in Multimodal Mixture-of-Experts LLMs

A new approach called UniMoE-Guided has been introduced, utilizing a knowledge-distilled multi-task Mixture-of-Experts (MoE) model for automated scoring of written responses. This model consolidates expertise from multiple task-specific large models into a single, efficient deployable model, enhancing performance while reducing resource demands.

A new study has introduced a Dynamic Mixture-of-Experts (MoE) approach aimed at addressing the challenges of continual and reinforcement learning, particularly in environments facing severe distribution shifts. This method seeks to enhance the adaptability of neural networks by dynamically adding capacity, inspired by the plasticity of biological brains, while also evaluating its effectiveness against existing network expansion techniques.

A new framework called AdaTok has been introduced to enhance the efficiency of Multimodal Large Language Models (MLLMs) by employing an object-level token merging strategy for adaptive token compression. This approach significantly reduces the number of tokens used, achieving approximately 96% of the performance of traditional models while utilizing only 10% of the tokens, addressing computational and memory burdens associated with patch-level tokenization.

CADTrack introduces a novel framework for RGB-Thermal tracking, addressing the challenges of modality discrepancies that hinder effective feature representation and tracking accuracy. The framework employs Mamba-based Feature Interaction and a Contextual Aggregation Module to enhance feature discrimination and reduce computational costs.

A new method for enhancing social interaction understanding in videos has been proposed, focusing on the alignment of verbal and non-verbal cues in multi-speaker scenarios. This approach addresses the limitations observed in existing Multimodal Large Language Models (MLLMs), which struggle with cross-modal attention consistency in such contexts.

A new algorithm named MM-Det++ has been proposed to enhance the detection of videos generated by diffusion models, addressing the growing concerns over synthetic media and information security. This algorithm integrates a Spatio-Temporal branch utilizing a Frame-Centric Vision Transformer and a Multimodal branch for improved detection capabilities.

The introduction of ChineseVideoBench marks a significant advancement in the evaluation of Multimodal Large Language Models (MLLMs) specifically for Chinese Video Question Answering. This benchmark provides a comprehensive dataset and tailored metrics, addressing the need for culturally-aware evaluation frameworks in video analysis.

A new study has introduced a multimodal visual understanding dataset (MSVQA) aimed at addressing catastrophic forgetting in Multimodal Large Language Models (MLLMs) by adapting to various scenarios such as high altitude, underwater, low altitude, and indoor settings. The proposed method, UNIFIER, seeks to enhance visual learning by decoupling visual information into distinct branches within each vision block.