DiCoBench: Benchmarking Multi-Image Fine-Grained Perception via Differential and Commonality Visual Cues
On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning
Abstract (arXiv)
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated impressive fine-grained perception capabilities. However, existing benchmarks predominantly rely on explicit textual cues or low-resolution inputs, failing to evaluate a model's ability to autonomously perceive implicit visual cues in high-resolution. To bridge this gap, we introduce DiCoBench, a comprehensive, multi-image high-resolution benchmark designed for cross-image fine-grained perception. DiCoBench consists of 765 meticulously curated samples categorized into two progressive tracks: Differential Visual Cues and Commonality Visual Cues, covering 8 distinct perception tasks. By formulating the benchmark as a multiple-choice question task and utilizing high-resolution imagery (approaching 2K), we eliminate evaluation metric bias and pose a substantial challenge to current state-of-the-art MLLMs. Our extensive evaluation of 18 diverse MLLMs reveals a striking performance gap compared to human accuracy (98.3%), with top-performing models struggling significantly with micro-scale detail capture. We believe DiCoBench will serve as a challenging testbed to drive future research in autonomous, high-resolution multi-image perception.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 22 of its 33 references that are in the map. First posted on arXiv 25 Jun 2026.
Builds on
22 of 33 references.
Closest ancestors · 8
Same direction, newest first
- 2025LLaVA-OneVision-1.5: Fully Open Framework for Democratized Multimodal Training28 Sep
- 2025From Easy to Hard: The MIR Benchmark for Progressive Interleaved Multi-Image Reasoning21 Sep
- 2024Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution18 Sep
- 2024Divide, Conquer and Combine: A Training-Free Framework for High-Resolution Image Perception in Multimodal Large Language Models28 Aug
- 2024MuirBench: A Comprehensive Benchmark for Robust Multi-image Understanding13 Jun
- 2024MileBench: Benchmarking MLLMs in Long Context29 Apr
- 2024How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites25 Apr
- 2024BLINK: Multimodal Large Language Models Can See but Not Perceive18 Apr
Other references · 14
- 2023MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGIcited by 2,044
- 2023V*: Guided Visual Search as a Core Mechanism in Multimodal LLMscited by 459
- 2024VLMEvalKit: An Open-Source Toolkit for Evaluating Large Multi-Modality Modelscited by 443
- 2025DeepEyes: Incentivizing "Thinking with Images" via Reinforcement Learningcited by 398
- 2025Mini-o3: Scaling Up Reasoning Patterns and Interaction Turns for Visual Searchcited by 116
- 2018Learning to Describe Differences Between Pairs of Similar Imagescited by 114
- 2025Traceable Evidence Enhanced Visual Grounded Reasoning: Evaluation and Methodologycited by 67
- 2019Expressing Visual Relationships via Languagecited by 49
- 2024MLLM-CompBench: A Comparative Reasoning Benchmark for Multimodal LLMscited by 32
- 2024VisMin: Visual Minimal-Change Understandingcited by 23
- 2024G-VEval: A Versatile Metric for Evaluating Image and Video Captions Using GPT-4ocited by 23
- 2025Migician: Revealing the Magic of Free-Form Multi-Image Grounding in Multimodal Large Language Modelscited by 22
2 more
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026PerceptionBench: Evaluating Atomic Visual Perception in Multimodal Large Language Models
- 2026Zooming without Zooming: Region-to-Image Distillation for Fine-Grained Multimodal Perception
- 2026VisualNeedle: Benchmarking Active Visual Search in Information-Dense Scenes
- 2026LOCUS: Local Visual Cue Search for Enhancing Fine-Grained Perception in Multimodal Large Language Models
- 2025HueManity: Probing Fine-Grained Visual Perception in MLLMs
- 2025DiG: Differential Grounding for Enhancing Fine-Grained Perception in Multimodal Large Language Model
- 2026OddGridBench: Exposing the Lack of Fine-Grained Visual Discrepancy Sensitivity in Multimodal Large Language Models
- 2026Seeing or Knowing? Visual Context Sensitivity in Multimodal Large Language Models