Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models
- URL: http://arxiv.org/abs/2312.02219v2
- Date: Wed, 12 Jun 2024 14:59:55 GMT
- Title: Behind the Magic, MERLIM: Multi-modal Evaluation Benchmark for Large Image-Language Models
- Authors: Andrés Villa, Juan Carlos León Alcázar, Alvaro Soto, Bernard Ghanem,
- Abstract summary: 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.
- Score: 50.653838482083614
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large Vision and Language Models have enabled significant advances in fully supervised and zero-shot visual tasks. These large architectures serve as the baseline to what is currently known as Instruction Tuning Large Vision and Language models (IT-LVLMs). IT-LVLMs are general-purpose multi-modal assistants whose responses are modulated by natural language instructions and visual data. Despite this versatility, IT-LVLM effectiveness in fundamental computer vision problems remains unclear, primarily due to the absence of a standardized evaluation benchmark. This paper introduces a Multi-modal Evaluation Benchmark named MERLIM, 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. Our results bring important insights on the performance of state-of-the-art IT-LVMLs including limitations at identifying fine-grained visual concepts, object hallucinations across tasks, and biases towards the language query. Our findings also suggest that these models have weak visual grounding, but manage to make adequate guesses from global visual patterns or language biases contained in the LLM component.
Related papers
- Response Wide Shut: Surprising Observations in Basic Vision Language Model Capabilities [30.176918208200604]
Vision-Language Models (VLMs) have emerged as general purpose tools for addressing a variety of complex computer vision problems.
These models have been shown to be highly capable, but also lacking some basic visual understanding skills.
This paper sets out to understand the limitations of SoTA VLMs on fundamental visual tasks.
arXiv Detail & Related papers (2024-08-13T08:26:32Z) - X-Former: Unifying Contrastive and Reconstruction Learning for MLLMs [49.30255148577368]
X-Former is a lightweight transformer module designed to exploit the complementary strengths of CL and MIM.
X-Former first bootstraps vision-language representation learning and multimodal-to-multimodal generative learning from two frozen vision encoders.
It further bootstraps vision-to-language generative learning from a frozen LLM to ensure visual features from X-Former can be interpreted by the LLM.
arXiv Detail & Related papers (2024-07-18T18:39:54Z) - Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMs [56.391404083287235]
We introduce Cambrian-1, a family of multimodal LLMs (MLLMs) designed with a vision-centric approach.
Our study uses LLMs and visual instruction tuning as an interface to evaluate various visual representations.
We provide model weights, code, supporting tools, datasets, and detailed instruction-tuning and evaluation recipes.
arXiv Detail & Related papers (2024-06-24T17:59:42Z) - 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) - Towards Multimodal In-Context Learning for Vision & Language Models [21.69457980865084]
State-of-the-art Vision-Language Models (VLMs) ground the vision and the language modality.
We propose a simple yet surprisingly effective multi-turn curriculum-based learning methodology with effective data mixes.
arXiv Detail & Related papers (2024-03-19T13:53:37Z) - Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models [87.47400128150032]
We propose a novel LMM architecture named Lumen, a Large multimodal model with versatile vision-centric capability enhancement.
Lumen first promotes fine-grained vision-language concept alignment.
Then the task-specific decoding is carried out by flexibly routing the shared representation to lightweight task decoders.
arXiv Detail & Related papers (2024-03-12T04:13:45Z) - CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models [58.95889895912716]
We introduce a new benchmark, named as CODIS, designed to assess the ability of models to use context provided in free-form text to enhance visual comprehension.
Our findings indicate that MLLMs consistently fall short of human performance on this benchmark.
This underscores the pressing need to enhance the ability of MLLMs to comprehend visuals in a context-dependent manner.
arXiv Detail & Related papers (2024-02-21T08:21:12Z) - Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs [50.77984109941538]
Our research reveals that the visual capabilities in recent multimodal LLMs still exhibit systematic shortcomings.
We identify ''CLIP-blind pairs'' - images that CLIP perceives as similar despite their clear visual differences.
We evaluate various CLIP-based vision-and-language models and found a notable correlation between visual patterns that challenge CLIP models and those problematic for multimodal LLMs.
arXiv Detail & Related papers (2024-01-11T18:58:36Z)
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.