CameraBench: Benchmarking Visual Reasoning in MLLMs via Photography
- URL: http://arxiv.org/abs/2504.10090v2
- Date: Thu, 17 Apr 2025 12:33:59 GMT
- Title: CameraBench: Benchmarking Visual Reasoning in MLLMs via Photography
- Authors: I-Sheng Fang, Jun-Cheng Chen,
- Abstract summary: Large language models (LLMs) and multimodal large language models (MLLMs) have significantly advanced artificial intelligence.<n>Recent advancements, including the reasoning models like OpenAI o1 and Gemini 2.0 Flash Thinking, have opened this capability.<n>We focus specifically on photography-related tasks because a photo is a visual snapshot of the physical world where the underlying physics interplay with the camera parameters.
- Score: 12.305953690308085
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) and multimodal large language models (MLLMs) have significantly advanced artificial intelligence. However, visual reasoning, reasoning involving both visual and textual inputs, remains underexplored. Recent advancements, including the reasoning models like OpenAI o1 and Gemini 2.0 Flash Thinking, which incorporate image inputs, have opened this capability. In this ongoing work, we focus specifically on photography-related tasks because a photo is a visual snapshot of the physical world where the underlying physics (i.e., illumination, blur extent, etc.) interplay with the camera parameters. Successfully reasoning from the visual information of a photo to identify these numerical camera settings requires the MLLMs to have a deeper understanding of the underlying physics for precise visual comprehension, representing a challenging and intelligent capability essential for practical applications like photography assistant agents. We aim to evaluate MLLMs on their ability to distinguish visual differences related to numerical camera settings, extending a methodology previously proposed for vision-language models (VLMs). Our preliminary results demonstrate the importance of visual reasoning in photography-related tasks. Moreover, these results show that no single MLLM consistently dominates across all evaluation tasks, demonstrating ongoing challenges and opportunities in developing MLLMs with better visual reasoning.
Related papers
- Seeing from Another Perspective: Evaluating Multi-View Understanding in MLLMs [41.072699990427374]
Multi-view understanding is a fundamental challenge in Multi-Modal Large Language Models (MLLMs) to be used as embodied agents.
We propose All-Angles Bench, a benchmark of over 2,100 human carefully annotated multi-view question-answer pairs across 90 real-world scenes.
Our experiments, benchmark on 27 representative MLLMs including Gemini-2.0-Flash, Claude-3.7-Sonnet, and GPT-4o against human evaluators reveals a substantial performance gap.
arXiv Detail & Related papers (2025-04-21T17:59:53Z) - VOILA: Evaluation of MLLMs For Perceptual Understanding and Analogical Reasoning [63.0285363282581]
Multimodal Large Language Models (MLLMs) have become a powerful tool for integrating visual and textual information.<n>We introduce VOILA, a benchmark designed to evaluate MLLMs' perceptual understanding and abstract relational reasoning.<n>We reveal that current MLLMs struggle to comprehend inter-image relationships and exhibit limited capabilities in high-level relational reasoning.
arXiv Detail & Related papers (2025-02-25T23:36:19Z) - MC-Bench: A Benchmark for Multi-Context Visual Grounding in the Era of MLLMs [61.56904387052982]
This paper proposes a new visual grounding task called multi-context visual grounding.
It aims to localize instances of interest across multiple images based on open-ended text prompts.
We benchmark over 20 state-of-the-art MLLMs and foundation models with potential multi-context visual grounding capabilities.
arXiv Detail & Related papers (2024-10-16T07:52:57Z) - Explore the Hallucination on Low-level Perception for MLLMs [83.12180878559295]
We aim to define and evaluate the self-awareness of MLLMs in low-level visual perception and understanding tasks.
We present QL-Bench, a benchmark settings to simulate human responses to low-level vision.
We demonstrate that while some models exhibit robust low-level visual capabilities, their self-awareness remains relatively underdeveloped.
arXiv Detail & Related papers (2024-09-15T14:38:29Z) - Rethinking Visual Prompting for Multimodal Large Language Models with External Knowledge [76.45868419402265]
multimodal large language models (MLLMs) have made significant strides by training on vast high-quality image-text datasets.
However, the inherent difficulty in explicitly conveying fine-grained or spatially dense information in text, such as masks, poses a challenge for MLLMs.
This paper proposes a new visual prompt approach to integrate fine-grained external knowledge, gleaned from specialized vision models, into MLLMs.
arXiv Detail & Related papers (2024-07-05T17:43:30Z) - Visualization Literacy of Multimodal Large Language Models: A Comparative Study [12.367399155606162]
multimodal large language models (MLLMs) combine the inherent power of large language models (LLMs) with the renewed capabilities to reason about the multimodal context.
Many recent works in visualization have demonstrated MLLMs' capability to understand and interpret visualization results and explain the content of the visualization to users in natural language.
In this work, we aim to fill the gap by utilizing the concept of visualization literacy to evaluate MLLMs.
arXiv Detail & Related papers (2024-06-24T17:52:16Z) - Draw-and-Understand: Leveraging Visual Prompts to Enable MLLMs to Comprehend What You Want [58.091825321168514]
We present the Draw-and-Understand framework, exploring how to integrate visual prompting understanding capabilities into Multimodal Large Language Models (MLLMs)<n>Visual prompts allow users to interact through multi-modal instructions, enhancing the models' interactivity and fine-grained image comprehension.<n>In this framework, we propose a general architecture adaptable to different pre-trained MLLMs, enabling it to recognize various types of visual prompts.
arXiv Detail & Related papers (2024-03-29T16:26:20Z) - Proximity QA: Unleashing the Power of Multi-Modal Large Language Models
for Spatial Proximity Analysis [45.62657605766754]
Multi-modal large language models (MLLMs) have demonstrated remarkable vision-language capabilities.
Proximity QA is a novel framework designed to enable MLLMs to infer the proximity relationship between objects in images.
We have conducted extensive experiments to validate Proximity QA's superior ability in depth perception and proximity analysis.
arXiv Detail & Related papers (2024-01-31T14:21:49Z) - AesBench: An Expert Benchmark for Multimodal Large Language Models on
Image Aesthetics Perception [64.25808552299905]
AesBench is an expert benchmark aiming to comprehensively evaluate the aesthetic perception capacities of MLLMs.
We construct an Expert-labeled Aesthetics Perception Database (EAPD), which features diversified image contents and high-quality annotations provided by professional aesthetic experts.
We propose a set of integrative criteria to measure the aesthetic perception abilities of MLLMs from four perspectives, including Perception (AesP), Empathy (AesE), Assessment (AesA) and Interpretation (AesI)
arXiv Detail & Related papers (2024-01-16T10:58:07Z) - Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models [50.653838482083614]
This paper introduces a scalable test-bed to assess the capabilities of IT-LVLMs on fundamental computer vision tasks.
MERLIM contains over 300K image-question pairs and has a strong focus on detecting cross-modal "hallucination" events in IT-LVLMs.
arXiv Detail & Related papers (2023-12-03T16:39:36Z) - Look, Remember and Reason: Grounded reasoning in videos with language
models [5.3445140425713245]
Multi-temporal language models (LM) have recently shown promising performance in high-level reasoning tasks on videos.
We propose training an LM end-to-end on low-level surrogate tasks, including object detection, re-identification, tracking, to endow the model with the required low-level visual capabilities.
We demonstrate the effectiveness of our framework on diverse visual reasoning tasks from the ACRE, CATER, Something-Else and STAR datasets.
arXiv Detail & Related papers (2023-06-30T16:31:14Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.