LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model

• URL: http://arxiv.org/abs/2304.15010v1
• Date: Fri, 28 Apr 2023 17:59:25 GMT
• Title: LLaMA-Adapter V2: Parameter-Efficient Visual Instruction Model
• Authors: Peng Gao, Jiaming Han, Renrui Zhang, Ziyi Lin, Shijie Geng, Aojun Zhou, Wei Zhang, Pan Lu, Conghui He, Xiangyu Yue, Hongsheng Li, Yu Qiao
• Abstract summary: We present LLaMA-Adapter V2, a parameter-efficient visual instruction model. Specifically, we first augment LLaMA-Adapter by unlocking more learnable parameters. Thirdly, a joint training paradigm of image-text pairs and instruction-following data is introduced.
• Score: 60.22693761583569
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: How to efficiently transform large language models (LLMs) into instruction followers is recently a popular research direction, while training LLM for multi-modal reasoning remains less explored. Although the recent LLaMA-Adapter demonstrates the potential to handle visual inputs with LLMs, it still cannot generalize well to open-ended visual instructions and lags behind GPT-4. In this paper, we present LLaMA-Adapter V2, a parameter-efficient visual instruction model. Specifically, we first augment LLaMA-Adapter by unlocking more learnable parameters (e.g., norm, bias and scale), which distribute the instruction-following ability across the entire LLaMA model besides adapters. Secondly, we propose an early fusion strategy to feed visual tokens only into the early LLM layers, contributing to better visual knowledge incorporation. Thirdly, a joint training paradigm of image-text pairs and instruction-following data is introduced by optimizing disjoint groups of learnable parameters. This strategy effectively alleviates the interference between the two tasks of image-text alignment and instruction following and achieves strong multi-modal reasoning with only a small-scale image-text and instruction dataset. During inference, we incorporate additional expert models (e.g. captioning/OCR systems) into LLaMA-Adapter to further enhance its image understanding capability without incurring training costs. Compared to the original LLaMA-Adapter, our LLaMA-Adapter V2 can perform open-ended multi-modal instructions by merely introducing 14M parameters over LLaMA. The newly designed framework also exhibits stronger language-only instruction-following capabilities and even excels in chat interactions. Our code and models are available at https://github.com/ZrrSkywalker/LLaMA-Adapter.

Related papers

• Looking Beyond Text: Reducing Language bias in Large Vision-Language Models via Multimodal Dual-Attention and Soft-Image Guidance [67.26434607115392]
  Large vision-language models (LVLMs) have achieved impressive results in various vision-language tasks. LVLMs suffer from hallucinations caused by language bias, leading to diminished focus on images and ineffective visual comprehension. We propose LACING to address the language bias of LVLMs with muLtimodal duAl-attention meChanIsm (MDA) aNd soft-image Guidance (IFG)
  arXiv  Detail & Related papers  (2024-11-21T16:33:30Z)
• SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs [40.74693126923826]
  Multimodal Large Language Models (MLLMs) have recently demonstrated remarkable perceptual and reasoning abilities. Training adapters with image-level supervision often results in significant misalignment. We introduce Supervised Embedding Alignment (SEA), a token-level alignment method that leverages vision-language pre-trained models.
  arXiv  Detail & Related papers  (2024-08-21T17:58:02Z)
• Adapting LLaMA Decoder to Vision Transformer [65.47663195233802]
  This work examines whether decoder-only Transformers such as LLaMA can be adapted to the computer vision field. We first "LLaMAfy" a standard ViT step-by-step to align with LLaMA's architecture, and find that directly applying a causal mask to the self-attention brings an attention collapse issue. We develop a soft mask strategy that gradually introduces a causal mask to the self-attention at the onset of training to facilitate the optimization behavior.
  arXiv  Detail & Related papers  (2024-04-10T06:30:08Z)
• LLaMA-Excitor: General Instruction Tuning via Indirect Feature Interaction [24.675876324457747]
  Existing methods to fine-tune LLMs, like Adapter, Prefix-tuning, and LoRA, may compromise the innate abilities of LLMs. We propose LLaMA-Excitor, a lightweight method that stimulates the LLMs' potential to better follow instructions by gradually paying more attention to worthwhile information. LLaMA-Excitor is the only method that maintains basic capabilities while achieving a significant improvement.
  arXiv  Detail & Related papers  (2024-04-01T04:39:21Z)
• 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)
• Cheap and Quick: Efficient Vision-Language Instruction Tuning for Large Language Models [77.2078051555533]
  We propose a novel and affordable solution for the effective VL adaption of large language models (LLMs) Instead of using large neural networks to connect the image encoder and LLM, MMA adopts lightweight modules, i.e., adapters. MMA is also equipped with a routing algorithm to help LLMs achieve an automatic shift between single- and multi-modal instructions.
  arXiv  Detail & Related papers  (2023-05-24T11:06:15Z)
• 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)
• LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention [52.6718081345361]
  LLaMA-Adapter is a method to efficiently fine-tune LLaMA into an instruction-following model. It introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs.
  arXiv  Detail & Related papers  (2023-03-28T17:59:12Z)

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.