CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models

• URL: http://arxiv.org/abs/2402.13607v3
• Date: Wed, 5 Jun 2024 02:14:06 GMT
• Title: CODIS: Benchmarking Context-Dependent Visual Comprehension for Multimodal Large Language Models
• Authors: Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang, Qidong Yan, Yingjie Li, Xiaolong Wang, Siyu Wang, Ziyue Wang, Xiaoyue Mi, Peng Li, Ning Ma, Maosong Sun, Yang Liu,
• Abstract summary: 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.
• Score: 58.95889895912716
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal large language models (MLLMs) have demonstrated promising results in a variety of tasks that combine vision and language. As these models become more integral to research and applications, conducting comprehensive evaluations of their capabilities has grown increasingly important. However, most existing benchmarks fail to consider that, in certain situations, images need to be interpreted within a broader context. In this work, 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. Further analysis confirms that these models struggle to effectively extract and utilize contextual information to improve their understanding of images. This underscores the pressing need to enhance the ability of MLLMs to comprehend visuals in a context-dependent manner. View our project website at https://thunlp-mt.github.io/CODIS.

Related papers

• EMMA: Efficient Visual Alignment in Multi-Modal LLMs [56.03417732498859]
  EMMA is a lightweight cross-modality module designed to efficiently fuse visual and textual encodings. EMMA boosts performance across multiple tasks by up to 9.3% while significantly improving robustness against hallucinations.
  arXiv  Detail & Related papers  (2024-10-02T23:00:31Z)
• 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)
• Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models [10.41857522464292]
  We introduce the MultiModal Needle-in-a-haystack (MMNeedle) benchmark to assess the long-context capabilities of MLLMs. We employ image stitching to further increase the input context length, and develop a protocol to automatically generate labels for sub-image level retrieval. We evaluate state-of-the-art MLLMs, encompassing both API-based and open-source models.
  arXiv  Detail & Related papers  (2024-06-17T05:54:06Z)
• VEGA: Learning Interleaved Image-Text Comprehension in Vision-Language Large Models [76.94378391979228]
  We introduce a new, more demanding task known as Interleaved Image-Text (IITC) This task challenges models to discern and disregard superfluous elements in both images and text to accurately answer questions. In support of this task, we further craft a new VEGA dataset, tailored for the IITC task on scientific content, and devised a subtask, Image-Text Association (ITA)
  arXiv  Detail & Related papers  (2024-06-14T17:59:40Z)
• Multi-modal Instruction Tuned LLMs with Fine-grained Visual Perception [63.03288425612792]
  We propose bfAnyRef, a general MLLM model that can generate pixel-wise object perceptions and natural language descriptions from multi-modality references. Our model achieves state-of-the-art results across multiple benchmarks, including diverse modality referring segmentation and region-level referring expression generation.
  arXiv  Detail & Related papers  (2024-03-05T13:45:46Z)
• Can Large Language Models Understand Context? [17.196362853457412]
  This paper introduces a context understanding benchmark by adapting existing datasets to suit the evaluation of generative models. Experimental results indicate that pre-trained dense models struggle with understanding more nuanced contextual features when compared to state-of-the-art fine-tuned models. As LLM compression holds growing significance in both research and real-world applications, we assess the context understanding of quantized models under in-context-learning settings.
  arXiv  Detail & Related papers  (2024-02-01T18:55:29Z)
• Veagle: Advancements in Multimodal Representation Learning [0.0]
  This paper introduces a novel approach to enhance the multimodal capabilities of existing models. Our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works. Our results indicate a improvement of 5-6 % in performance, with Veagle outperforming existing models by a notable margin.
  arXiv  Detail & Related papers  (2024-01-18T12:45:25Z)
• 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)
• Position-Enhanced Visual Instruction Tuning for Multimodal Large Language Models [50.07056960586183]
  We propose Position-enhanced Visual Instruction Tuning (PVIT) to extend the functionality of Multimodal Large Language Models (MLLMs) This integration promotes a more detailed comprehension of images for the MLLM. We present both quantitative experiments and qualitative analysis that demonstrate the superiority of the proposed model.
  arXiv  Detail & Related papers  (2023-08-25T15:33:47Z)
• Scalable Performance Analysis for Vision-Language Models [26.45624201546282]
  Joint vision-language models have shown great performance over a diverse set of tasks. Our paper introduces a more scalable solution that relies on already annotated benchmarks. We confirm previous findings that CLIP behaves like a bag of words model and performs better with nouns and verbs.
  arXiv  Detail & Related papers  (2023-05-30T06:40:08Z)

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.