The field of artificial intelligence is witnessing significant advancements in the development of Mixture-of-Experts (MoE) models and multimodal learning techniques. Researchers are exploring innovative ways to improve the efficiency, scalability, and performance of these models. One notable direction is the use of MoE models for visual tracking, zero-shot quantization, and multimodal information acquisition. These models have shown promising results in various applications, including image and video processing, natural language processing, and computer vision. Another area of focus is the development of unified frameworks for spatiotemporal learning, which can handle diverse tasks and datasets. The use of transformer-based architectures and adaptive regulation techniques is also becoming increasingly popular. Noteworthy papers in this area include SPMTrack, which proposes a novel tracker based on MoE tailored for visual tracking, and GranQ, which introduces a granular zero-shot quantization approach with unified layer-channel awareness. Additionally, papers like TARDIS and UniSTD demonstrate the potential of representation steering and unified spatiotemporal learning for mitigating temporal misalignment and improving cross-task learning. Overall, the field is moving towards more efficient, scalable, and versatile models that can handle complex tasks and datasets.
Advances in Mixture-of-Experts and Multimodal Learning
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SPMTrack: Spatio-Temporal Parameter-Efficient Fine-Tuning with Mixture of Experts for Scalable Visual Tracking
GranQ: Granular Zero-Shot Quantization with Unified Layer-Channel Awareness
Adaptive Unimodal Regulation for Balanced Multimodal Information Acquisition
TARDIS: Mitigate Temporal Misalignment via Representation Steering
Enhancing Multi-modal Models with Heterogeneous MoE Adapters for Fine-tuning
UniSTD: Towards Unified Spatio-Temporal Learning across Diverse Disciplines
Optimal Scaling Laws for Efficiency Gains in a Theoretical Transformer-Augmented Sectional MoE Framework
Multimodal Image Matching based on Frequency-domain Information of Local Energy Response
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness
LLaVA-CMoE: Towards Continual Mixture of Experts for Large Vision-Language Models
UFM: Unified Feature Matching Pre-training with Multi-Modal Image Assistants
Exploiting Mixture-of-Experts Redundancy Unlocks Multimodal Generative Abilities