Towards Versatile and Efficient Visual Knowledge Integration into Pre-trained Language Models with Cross-Modal Adapters

• URL: http://arxiv.org/abs/2305.07358v4
• Date: Fri, 16 Feb 2024 02:55:03 GMT
• Title: Towards Versatile and Efficient Visual Knowledge Integration into Pre-trained Language Models with Cross-Modal Adapters
• Authors: Xinyun Zhang, Haochen Tan, Han Wu, Bei Yu
• Abstract summary: We propose a new plug-and-play module, X-adapter, to leverage the aligned visual and textual knowledge learned in pre-trained vision-language models. Our method can significantly improve the performance on object-color reasoning and natural language understanding tasks.
• Score: 16.44174900423759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Humans learn language via multi-modal knowledge. However, due to the text-only pre-training scheme, most existing pre-trained language models (PLMs) are hindered from the multi-modal information. To inject visual knowledge into PLMs, existing methods incorporate either the text or image encoder of vision-language models (VLMs) to encode the visual information and update all the original parameters of PLMs for knowledge fusion. In this paper, we propose a new plug-and-play module, X-adapter, to flexibly leverage the aligned visual and textual knowledge learned in pre-trained VLMs and efficiently inject them into PLMs. Specifically, we insert X-adapters into PLMs, and only the added parameters are updated during adaptation. To fully exploit the potential in VLMs, X-adapters consist of two sub-modules, V-expert and T-expert, to fuse VLMs' image and text representations, respectively. We can opt for activating different sub-modules depending on the downstream tasks. Experimental results show that our method can significantly improve the performance on object-color reasoning and natural language understanding (NLU) tasks compared with PLM baselines.

Related papers

• 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)
• Improving Visual Commonsense in Language Models via Multiple Image Generation [41.565399860320966]
  Existing large language models (LLMs) are primarily trained using textual data only. Visual Language Models, which excel at visually-oriented tasks, often fail at non-visual tasks such as basic commonsense reasoning. This divergence highlights a critical challenge - the integration of robust visual understanding with foundational text-based language reasoning.
  arXiv  Detail & Related papers  (2024-06-19T15:17:10Z)
• Optimization of Prompt Learning via Multi-Knowledge Representation for Vision-Language Models [26.964848679914354]
  CoKnow is a framework that enhances Prompt Learning for Vision-Language Models with rich contextual knowledge. We conducted extensive experiments on 11 publicly available datasets, demonstrating that CoKnow outperforms a series of previous methods.
  arXiv  Detail & Related papers  (2024-04-16T07:44:52Z)
• VILA: On Pre-training for Visual Language Models [74.08039416548209]
  We study the design options for VLM pre-training through step-by-step controllable comparisons. We build VILA, a Visual Language model family that consistently outperforms the state-of-the-art models.
  arXiv  Detail & Related papers  (2023-12-12T18:58:18Z)
• InfMLLM: A Unified Framework for Visual-Language Tasks [44.29407348046122]
  multimodal large language models (MLLMs) have attracted growing interest. This work delves into enabling LLMs to tackle more vision-language-related tasks. InfMLLM achieves either state-of-the-art (SOTA) performance or performance comparable to recent MLLMs.
  arXiv  Detail & Related papers  (2023-11-12T09:58:16Z)
• Frozen Transformers in Language Models Are Effective Visual Encoder Layers [26.759544759745648]
  Large language models (LLMs) are surprisingly strong encoders for purely visual tasks in the absence of language. Our work pushes the boundaries of leveraging LLMs for computer vision tasks. We propose the information filtering hypothesis to explain the effectiveness of pre-trained LLMs in visual encoding.
  arXiv  Detail & Related papers  (2023-10-19T17:59:05Z)
• Context-Aware Prompt Tuning for Vision-Language Model with Dual-Alignment [15.180715595425864]
  We introduce a novel method to improve the prompt learning of vision-language models by incorporating pre-trained large language models (LLMs) With DuAl-PT, we propose to learn more context-aware prompts, benefiting from both explicit and implicit context modeling. Empirically, DuAl-PT achieves superior performance on 11 downstream datasets on few-shot recognition and base-to-new generalization.
  arXiv  Detail & Related papers  (2023-09-08T06:51:15Z)
• 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)
• Adapting Pre-trained Language Models to Vision-Language Tasks via Dynamic Visual Prompting [83.21164539349273]
  Pre-trained language models (PLMs) have played an increasing role in multimedia research. In this paper, we focus on exploring PLMs as a stand-alone model for vision-language reasoning tasks. We propose a novel transfer learning approach for PLMs, termed Dynamic Visual Prompting (DVP)
  arXiv  Detail & Related papers  (2023-06-01T07:19:28Z)
• LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation [51.08810811457617]
  vision-language alignment in LLMs is actively being researched to enable multimodal reasoning and visual IO. We develop a method for instruction-tuning an LLM only on text to gain vision-language capabilities for medical images. Our model, LLM-CXR, trained in this approach shows better image-text alignment in both CXR understanding and generation tasks.
  arXiv  Detail & Related papers  (2023-05-19T07:44:39Z)
• mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality [95.76661165594884]
  mPLUG-Owl is a training paradigm that equips large language models (LLMs) with multi-modal abilities. The training paradigm involves a two-stage method for aligning image and text, which learns visual knowledge with the assistance of LLM. Experimental results show that our model outperforms existing multi-modal models.
  arXiv  Detail & Related papers  (2023-04-27T13:27:01Z)

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.