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Unlocking Multimodal AI: How LLMs Can See and Hear Without Training

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Unlocking Multimodal AI: How LLMs Can See and Hear Without Training

Recent breakthroughs in artificial intelligence reveal that large language models (LLMs) possess inherent capabilities to process visual and auditory information, even without specialized training. This article explores the open-source MILS framework, demonstrating how LLMs can perform image captioning, audio analysis, and video understanding tasks in a zero-shot learning paradigm.

Core Technical Insights

The methodology from the paper “LLMs Can See and Hear Without Any Training” introduces three key innovations:

  1. Cross-Modal Embedding Alignment
    Leverages pre-trained models to map multimodal data into a unified semantic space

  2. Dynamic Prompt Engineering
    Translates visual/audio features into natural language prompts for LLM comprehension

  3. Parameter-Efficient Inference
    Executes tasks without fine-tuning, preserving original model weights

Multimodal Processing Workflow

Implementation Guide

Environment Setup

conda env create -f environment.yml
conda activate MILS

Essential Datasets

Dataset Purpose Download Command
MS-COCO Image caption benchmark wget http://images.cocodataset.org/zips/val2014.zip
Clotho Audio understanding wget https://zenodo.org/records/3490684/files/clotho_audio_evaluation.7z
MSR-VTT Video analysis wget https://www.robots.ox.ac.uk/~maxbain/frozen-in-time/data/MSRVTT.zip

Update directory paths in paths.py after downloading datasets.

Practical Applications

Image Captioning

Run distributed processing on 8 GPUs:

CUDA_VISIBLE_DEVICES=0 python main_image_captioning.py --process 0 --num_processes 8 --batch_size 32
  • Outputs saved to OUTPUT_DIR
  • Evaluate metrics:
python eval/image_captioning.py

Audio Content Understanding

Supports WAV/MP3 input with automatic spectrogram conversion:

python main_audio_captioning.py --process 0 --num_processes 8 --batch_size 32

Video Analysis

Requires ViClip pretrained weights:

wget https://huggingface.co/OpenGVLab/ViCLIP/blob/main/ViClip-InternVid-10M-FLT.pth

Process 5-second clips by default:

python main_video_captioning.py --clip_duration 5

Advanced Use Cases

Cross-Modal Arithmetic

  1. Convert images/audio to text descriptions
  2. Generate new images via combined prompts:
python main_image_generation_enhancement.py --prompt "sunset over mountains with birdsong"

Style Transfer

Combine content and style images:

python main_style_transfer.py \
    --style_image impressionism.jpg \
    --content_image landscape.jpg

Performance Optimization

  1. Memory Management
    Recommended batch sizes: 32 (images), 4-8 (videos)

  2. Distributed Processing
    Processes 50k images in ~45 minutes on 8×A100 GPUs

  3. Caching Mechanism
    First-run feature extraction enables 3-5× speedups in subsequent executions

Benchmark Results

Task Metric Improvement
Image Captioning +12.7% CIDEr
Audio Description +9.3% BLEU-4
Video Analysis 8× faster inference

Practical Implications

  1. Accessibility Tech
    Real-time environment description for visually impaired users

  2. Content Moderation
    Multimodal detection of policy violations

  3. Educational Tools
    Interactive learning through mixed media

FAQ

Q: Does this require training vision/audio modules?
A: Fully zero-shot implementation with frozen parameters

Q: Minimum hardware requirements?
A: Single GPU with 24GB VRAM (e.g., RTX 3090) for basic tasks

Q: Commercial use limitations?
A: CC-BY-NC 4.0 license requires additional permissions for commercial applications

@article{ashutosh2025llms,
  title={LLMs can see and hear without any training},
  author={Ashutosh, Kumar and Gandelsman, Yossi and Chen, Xinlei and Misra, Ishan and Girdhar, Rohit},
  journal={arXiv preprint arXiv:2501.18096},
  year={2025}
}
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