Efficiency and Sustainability in Vision-Language Models

The recent advancements in Vision-Language Models (VLMs) have primarily focused on enhancing efficiency without compromising performance, particularly in resource-constrained environments. Researchers are increasingly exploring methods to reduce computational costs associated with visual tokens, which are often redundant and computationally expensive. Key strategies include dynamic token pruning, adaptive token merging, and leveraging smaller models to guide larger ones, all aimed at optimizing inference speed and reducing computational load. Notably, the integration of lightweight hyper-networks and adaptive pruning strategies has shown significant promise in maintaining model accuracy while drastically reducing the number of visual tokens. Additionally, the use of training-free methods and early exiting mechanisms has been highlighted as effective approaches to accelerate inference without additional computational overhead. These developments not only address the efficiency challenges but also pave the way for more sustainable AI practices by minimizing carbon emissions. The field is moving towards more adaptive and context-aware models that can dynamically adjust their computational demands based on the input data, thereby enhancing both performance and resource utilization.

Noteworthy papers include 'Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings,' which introduces a method to dynamically remove visual tokens based on text token status, and 'AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning,' which proposes a training-free adaptive inference method for multi-modal LLMs that significantly reduces computation load while preserving performance.

Sources

Accelerating Multimodal Large Language Models via Dynamic Visual-Token Exit and the Empirical Findings

ATP-LLaVA: Adaptive Token Pruning for Large Vision Language Models

Accelerating Multimodel Large Language Models by Searching Optimal Vision Token Reduction

Early Exit Is a Natural Capability in Transformer-based Models: An Empirical Study on Early Exit without Joint Optimization

VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models

[CLS] Attention is All You Need for Training-Free Visual Token Pruning: Make VLM Inference Faster

CEGI: Measuring the trade-off between efficiency and carbon emissions for SLMs and VLMs

AIM: Adaptive Inference of Multi-Modal LLMs via Token Merging and Pruning

A Stitch in Time Saves Nine: Small VLM is a Precise Guidance for accelerating Large VLMs

Not All Adapters Matter: Selective Adapter Freezing for Memory-Efficient Fine-Tuning of Language Models

FlashSloth: Lightning Multimodal Large Language Models via Embedded Visual Compression

VisionZip: Longer is Better but Not Necessary in Vision Language Models

NVILA: Efficient Frontier Visual Language Models

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