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Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models logo

Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models

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Training-free framework for high-resolution image perception in MLLMs.

FreeFree tier
Inputs: imageOutputs: text
Type
Open Source
Visit Website GitHub (40 stars)

About Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models

Divide, Conquer and Combine (DC²) is a training-free framework designed to enhance multimodal large language models' (MLLMs) perception of high-resolution images. It introduces HR-Bench, the first benchmark specifically evaluating MLLMs on 4K and 8K resolution images. The framework operates in three stages: dividing the high-resolution image into recursively partitioned and merged patches, conquering each patch by generating accurate textual descriptions via the MLLM, and combining these descriptions to improve the MLLM's understanding of the overall image. Experiments show that DC² yields relative improvements of +6% on HR-Bench and +8% on general multimodal benchmarks, effectively compensating for visual information loss caused by downsampling.

Key Features

Training-free framework for high-resolution image perception in multimodal large language models
Three-stage approach: Divide (recursive partitioning and merging of patches), Conquer (textual description generation per patch), Combine (integration of text for overall understanding)
Leverages complementary text modality to compensate for visual information loss from downsampling
Introduction of HR-Bench, a benchmark for evaluating MLLMs on 4K and 8K resolution images
Consistent and significant improvements: +6% relative accuracy on HR-Bench and +8% on general multimodal benchmarks

Pros & Cons

Pros
  • No training or fine-tuning required, reducing computational cost
  • Works with any existing MLLM without modifying the model architecture
  • Introduces a dedicated benchmark (HR-Bench) for high-resolution image evaluation
  • Demonstrates consistent and significant relative improvements across multiple benchmarks
Cons
  • Relies on the quality of text descriptions generated by the MLLM
  • Recursive partitioning may introduce computational overhead depending on image size and patch count
  • Benchmark limited to 4K/8K resolutions; effectiveness on ultra-high resolutions beyond 8K not yet demonstrated

Best For

Evaluating and benchmarking multimodal LLMs on high-resolution (4K/8K) imagesEnhancing MLLM-based image understanding tasks without additional training or fine-tuningImproving performance of existing MLLMs on high-resolution multimodal perception tasks

FAQ

What is DC²?
DC² stands for Divide, Conquer and Combine, a training-free framework for enhancing high-resolution image perception in multimodal large language models (MLLMs).
What benchmark was introduced in this work?
The authors introduced HR-Bench, the first benchmark specifically designed to evaluate MLLM performance on 4K and 8K resolution images.
How does DC² work?
It recursively partitions the high-resolution image into patches (Divide), generates textual descriptions for each patch using the MLLM (Conquer), and then combines the descriptions to improve the MLLM's overall understanding of the image (Combine).