Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves

• URL: http://arxiv.org/abs/2412.11509v1
• Date: Mon, 16 Dec 2024 07:33:23 GMT
• Title: Skip Tuning: Pre-trained Vision-Language Models are Effective and Efficient Adapters Themselves
• Authors: Shihan Wu, Ji Zhang, Pengpeng Zeng, Lianli Gao, Jingkuan Song, Heng Tao Shen,
• Abstract summary: We propose Skip Tuning as a novel paradigm for adapting vision-language models to downstream tasks. Unlike existing PT or adapter-based methods, Skip Tuning applies Layer-wise Skipping (LSkip) and Class-wise Skipping (CSkip) upon the FT baseline without introducing extra context vectors or adapter modules.
• Score: 123.07450481623124
• License:
• Abstract: Prompt tuning (PT) has long been recognized as an effective and efficient paradigm for transferring large pre-trained vision-language models (VLMs) to downstream tasks by learning a tiny set of context vectors. Nevertheless, in this work, we reveal that freezing the parameters of VLMs during learning the context vectors neither facilitates the transferability of pre-trained knowledge nor improves the memory and time efficiency significantly. Upon further investigation, we find that reducing both the length and width of the feature-gradient propagation flows of the full fine-tuning (FT) baseline is key to achieving effective and efficient knowledge transfer. Motivated by this, we propose Skip Tuning, a novel paradigm for adapting VLMs to downstream tasks. Unlike existing PT or adapter-based methods, Skip Tuning applies Layer-wise Skipping (LSkip) and Class-wise Skipping (CSkip) upon the FT baseline without introducing extra context vectors or adapter modules. Extensive experiments across a wide spectrum of benchmarks demonstrate the superior effectiveness and efficiency of our Skip Tuning over both PT and adapter-based methods. Code: https://github.com/Koorye/SkipTuning.

Related papers

• Reference Trustable Decoding: A Training-Free Augmentation Paradigm for Large Language Models [79.41139393080736]
  Large language models (LLMs) have rapidly advanced and demonstrated impressive capabilities. In-Context Learning (ICL) and. Efficient Fine-Tuning (PEFT) are currently two mainstream methods for augmenting. LLMs to downstream tasks. We propose Reference Trustable Decoding (RTD), a paradigm that allows models to quickly adapt to new tasks without fine-tuning.
  arXiv  Detail & Related papers  (2024-09-30T10:48:20Z)
• iConFormer: Dynamic Parameter-Efficient Tuning with Input-Conditioned Adaptation [15.97351561456467]
  In this paper, we propose a novel PEFT approach, input-Conditioned transFormer, termed iConFormer. We introduce an input-Conditioned Network (iCoN) in the dynamic adapter that enables instance-level feature transformation. To be specific, iCoN generates channel-wise convolutional kernels for each feature and transform it using adaptive convolution process to effectively capture task-specific and fine-grained details tailor to downstream tasks.
  arXiv  Detail & Related papers  (2024-09-04T16:06:23Z)
• Dynamic Tuning Towards Parameter and Inference Efficiency for ViT Adaptation [67.13876021157887]
  Dynamic Tuning (DyT) is a novel approach to improve both parameter and inference efficiency for ViT adaptation. DyT achieves superior performance compared to existing PEFT methods while evoking only 71% of their FLOPs on the VTAB-1K benchmark.
  arXiv  Detail & Related papers  (2024-03-18T14:05:52Z)
• Fast Trainable Projection for Robust Fine-Tuning [36.51660287722338]
  Robust fine-tuning aims to achieve competitive in-distribution (ID) performance. Projection-based fine-tuning has been successfully used in robust fine-tuning. Fast Trainable Projection is a new projection-based fine-tuning algorithm.
  arXiv  Detail & Related papers  (2023-10-29T22:52:43Z)
• Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization [71.87335804334616]
  Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. The training process of Large Language Models (LLMs) generally incurs the update of significant parameters. This paper proposes an efficient partial prompt tuning approach to improve performance and efficiency simultaneously.
  arXiv  Detail & Related papers  (2023-10-23T16:37:59Z)
• Approximated Prompt Tuning for Vision-Language Pre-trained Models [54.326232586461614]
  In vision-language pre-trained models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks. We propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning.
  arXiv  Detail & Related papers  (2023-06-27T05:43:47Z)
• On Transferability of Prompt Tuning for Natural Language Understanding [63.29235426932978]
  We investigate the transferability of soft prompts across different tasks and models. We find that trained soft prompts can well transfer to similar tasks and initialize PT for them to accelerate training and improve performance. Our findings show that improving PT with knowledge transfer is possible and promising, while prompts' cross-task transferability is generally better than the cross-model transferability.
  arXiv  Detail & Related papers  (2021-11-12T13:39:28Z)
• On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation [36.37565646597464]
  adapter-based tuning works by adding light-weight adapter modules to a pretrained language model (PrLM) It adds only a few trainable parameters per new task, allowing a high degree of parameter sharing. We demonstrate that adapter-based tuning outperforms fine-tuning on low-resource and cross-lingual tasks.
  arXiv  Detail & Related papers  (2021-06-06T16:10:12Z)

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.