CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs

• URL: http://arxiv.org/abs/2401.02582v1
• Date: Fri, 5 Jan 2024 00:26:07 GMT
• Title: CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs
• Authors: Daoan Zhang, Junming Yang, Hanjia Lyu, Zijian Jin, Yuan Yao, Mingkai Chen, Jiebo Luo
• Abstract summary: The research focuses on two aspects: first, image-to-image matching, and second, multi-image-to-text matching. We conduct evaluations on a range of both open-source and closed-source large models, including GPT-4V, Gemini, OpenFlamingo, and MMICL.
• Score: 48.269363759989915
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: When exploring the development of Artificial General Intelligence (AGI), a critical task for these models involves interpreting and processing information from multiple image inputs. However, Large Multimodal Models (LMMs) encounter two issues in such scenarios: (1) a lack of fine-grained perception, and (2) a tendency to blend information across multiple images. We first extensively investigate the capability of LMMs to perceive fine-grained visual details when dealing with multiple input images. The research focuses on two aspects: first, image-to-image matching (to evaluate whether LMMs can effectively reason and pair relevant images), and second, multi-image-to-text matching (to assess whether LMMs can accurately capture and summarize detailed image information). We conduct evaluations on a range of both open-source and closed-source large models, including GPT-4V, Gemini, OpenFlamingo, and MMICL. To enhance model performance, we further develop a Contrastive Chain-of-Thought (CoCoT) prompting approach based on multi-input multimodal models. This method requires LMMs to compare the similarities and differences among multiple image inputs, and then guide the models to answer detailed questions about multi-image inputs based on the identified similarities and differences. Our experimental results showcase CoCoT's proficiency in enhancing the multi-image comprehension capabilities of large multimodal models.

Related papers

• MMIU: Multimodal Multi-image Understanding for Evaluating Large Vision-Language Models [76.1999277491816]
  Multimodal Multi-image Understanding (MMIU) is a comprehensive evaluation suite designed to assess Large Vision-Language Models (LVLMs) MMIU encompasses 7 types of multi-image relationships, 52 tasks, 77K images, and 11K meticulously curated multiple-choice questions. Our evaluation of 24 popular LVLMs, including both open-source and proprietary models, reveals significant challenges in multi-image comprehension.
  arXiv  Detail & Related papers  (2024-08-05T17:56:41Z)
• Matryoshka Multimodal Models [92.41824727506751]
  We propose M3: Matryoshka Multimodal Models, which learns to represent visual content as nested sets of visual tokens. We find that COCO-style benchmarks only need around 9 visual tokens to obtain accuracy similar to that of using all 576 tokens.
  arXiv  Detail & Related papers  (2024-05-27T17:59:56Z)
• MM1: Methods, Analysis & Insights from Multimodal LLM Pre-training [103.72844619581811]
  We build performant Multimodal Large Language Models (MLLMs) In particular, we study the importance of various architecture components and data choices. We demonstrate that for large-scale multimodal pre-training using a careful mix of image-caption, interleaved image-text, and text-only data.
  arXiv  Detail & Related papers  (2024-03-14T17:51:32Z)
• DialogGen: Multi-modal Interactive Dialogue System for Multi-turn Text-to-Image Generation [46.085482021301516]
  We propose DialogGen to align off-the-shelf MLLMs and T2I models to build a Multi-modal Interactive Dialogue System. It is composed of drawing prompt alignment, careful training data curation, and error correction. Our experiments on DialogGen and user study demonstrate the effectiveness of DialogGen compared with other State-of-the-Art models.
  arXiv  Detail & Related papers  (2024-03-13T18:00:01Z)
• Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion [70.9767518332692]
  Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive performance across diverse vision-language tasks. However, they fall short to comprehend context involving multiple images. We propose a two phase paradigm, browse-and-concentrate, to enable in-depth multimodal context fusion.
  arXiv  Detail & Related papers  (2024-02-19T14:59:07Z)
• MM-Interleaved: Interleaved Image-Text Generative Modeling via Multi-modal Feature Synchronizer [106.79844459065828]
  This paper presents MM-Interleaved, an end-to-end generative model for interleaved image-text data. It introduces a multi-scale and multi-image feature synchronizer module, allowing direct access to fine-grained image features in the previous context. Experiments demonstrate the versatility of MM-Interleaved in recognizing visual details following multi-modal instructions and generating consistent images following both textual and visual conditions.
  arXiv  Detail & Related papers  (2024-01-18T18:50:16Z)

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.