Efficient Multimodal Learning and Inference

Introduction

The field of multimodal learning is rapidly advancing, with a focus on improving efficiency and reducing costs. Recent developments have led to the creation of more effective models and inference methods, enabling faster and more accurate processing of multimodal data.

General Direction

The field is moving towards the development of more efficient and scalable multimodal models, with a focus on reducing parameter sizes and improving inference speeds. This is being achieved through the use of techniques such as layer pruning, knowledge distillation, and elastic parallelism. Additionally, there is a growing interest in the development of novel architectures and training methods that can effectively capture and leverage the hierarchical structure of visual-semantic concepts.

Innovative Results

Researchers are exploring new approaches to multimodal learning, including the use of masked image modeling and hyperbolic space techniques. These methods have shown promising results in terms of efficiency and accuracy, and are likely to have a significant impact on the field. Furthermore, the development of dynamic expansion strategies and rehearsal-free generative retrieval methods is enabling more efficient and effective updating of model-based indexes.

Noteworthy Papers

  • PUMA: Layer-Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning, which proposes a novel layer-pruning method for reducing parameter sizes and improving inference speeds.
  • HMID-Net: An Exploration of Masked Image Modeling and Knowledge Distillation in Hyperbolic Space, which introduces a new approach to multimodal learning using hyperbolic space techniques.
  • ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism, which presents a novel serving paradigm for multimodal models that enables more efficient and scalable inference.
  • MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora, which proposes a dynamic expansion strategy for efficient updating of model-based indexes.
  • Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models, which introduces a novel monolithic multimodal large language model that achieves competitive performance while reducing training and inference costs.

Sources

PUMA: Layer-Pruned Language Model for Efficient Unified Multimodal Retrieval with Modality-Adaptive Learning

DS@GT at LongEval: Evaluating Temporal Performance in Web Search Systems and Topics with Two-Stage Retrieval

Pre-Training LLMs on a budget: A comparison of three optimizers

HMID-Net: An Exploration of Masked Image Modeling and Knowledge Distillation in Hyperbolic Space

MixLoRA-DSI: Dynamically Expandable Mixture-of-LoRA Experts for Rehearsal-Free Generative Retrieval over Dynamic Corpora

ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism

Zorse: Optimizing LLM Training Efficiency on Heterogeneous GPU Clusters

Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures

Seq vs Seq: An Open Suite of Paired Encoders and Decoders

NineToothed: A Triton-Based High-Level Domain-Specific Language for Machine Learning

Mono-InternVL-1.5: Towards Cheaper and Faster Monolithic Multimodal Large Language Models

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