Diversity-Aware Meta Visual Prompting

• URL: http://arxiv.org/abs/2303.08138v1
• Date: Tue, 14 Mar 2023 17:59:59 GMT
• Title: Diversity-Aware Meta Visual Prompting
• Authors: Qidong Huang and Xiaoyi Dong and Dongdong Chen and Weiming Zhang and Feifei Wang and Gang Hua and Nenghai Yu
• Abstract summary: We present Diversity-Aware Meta Visual Prompting(DAM-VP), an efficient prompting method for transferring pre-trained models to downstream tasks with frozen backbone. We cluster the downstream dataset into small subsets in a diversity-strapped way, with each subset has its own prompt separately. All the prompts are optimized with a meta-prompt, which is learned across several datasets.
• Score: 111.75306320834629
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present Diversity-Aware Meta Visual Prompting~(DAM-VP), an efficient and effective prompting method for transferring pre-trained models to downstream tasks with frozen backbone. A challenging issue in visual prompting is that image datasets sometimes have a large data diversity whereas a per-dataset generic prompt can hardly handle the complex distribution shift toward the original pretraining data distribution properly. To address this issue, we propose a dataset Diversity-Aware prompting strategy whose initialization is realized by a Meta-prompt. Specifically, we cluster the downstream dataset into small homogeneity subsets in a diversity-adaptive way, with each subset has its own prompt optimized separately. Such a divide-and-conquer design reduces the optimization difficulty greatly and significantly boosts the prompting performance. Furthermore, all the prompts are initialized with a meta-prompt, which is learned across several datasets. It is a bootstrapped paradigm, with the key observation that the prompting knowledge learned from previous datasets could help the prompt to converge faster and perform better on a new dataset. During inference, we dynamically select a proper prompt for each input, based on the feature distance between the input and each subset. Through extensive experiments, our DAM-VP demonstrates superior efficiency and effectiveness, clearly surpassing previous prompting methods in a series of downstream datasets for different pretraining models. Our code is available at: \url{https://github.com/shikiw/DAM-VP}.

Related papers

• Adapt-$\infty$: Scalable Lifelong Multimodal Instruction Tuning via Dynamic Data Selection [89.42023974249122]
  Adapt-$infty$ is a new multi-way and adaptive data selection approach for Lifelong Instruction Tuning. We construct pseudo-skill clusters by grouping gradient-based sample vectors. We select the best-performing data selector for each skill cluster from a pool of selector experts.
  arXiv  Detail & Related papers  (2024-10-14T15:48:09Z)
• QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learning [58.767866109043055]
  We introduce Query-dependent Prompt Optimization (QPO), which iteratively fine-tune a small pretrained language model to generate optimal prompts tailored to the input queries. We derive insights from offline prompting demonstration data, which already exists in large quantities as a by-product of benchmarking diverse prompts on open-sourced tasks. Experiments on various LLM scales and diverse NLP and math tasks demonstrate the efficacy and cost-efficiency of our method in both zero-shot and few-shot scenarios.
  arXiv  Detail & Related papers  (2024-08-20T03:06:48Z)
• Less is More: High-value Data Selection for Visual Instruction Tuning [127.38740043393527]
  We propose a high-value data selection approach TIVE, to eliminate redundancy within the visual instruction data and reduce the training cost. Our approach using only about 15% data can achieve comparable average performance to the full-data fine-tuned model across eight benchmarks.
  arXiv  Detail & Related papers  (2024-03-14T16:47:25Z)
• Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning [47.02160072880698]
  We introduce a self-evolving mechanism that allows the model itself to actively sample subsets that are equally or even more effective. The key to our data sampling technique lies in the enhancement of diversity in the chosen subsets. Extensive experiments across three datasets and benchmarks demonstrate the effectiveness of DiverseEvol.
  arXiv  Detail & Related papers  (2023-11-14T14:10:40Z)
• Distribution-Aware Prompt Tuning for Vision-Language Models [20.02599087680773]
  A key to prompt tuning is the feature space alignment between two modalities via learnable vectors with model parameters fixed. Inspired by this observation, we proposed distribution-aware prompt tuning (DAPT) for vision-language models. Our experiments on 11 benchmark datasets demonstrate that our method significantly improves generalizability.
  arXiv  Detail & Related papers  (2023-09-06T23:49:11Z)
• Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias [92.41919689753051]
  Large language models (LLMs) have been recently leveraged as training data generators for various natural language processing (NLP) tasks. We investigate training data generation with diversely attributed prompts, which have the potential to yield diverse and attributed generated data. We show that attributed prompts outperform simple class-conditional prompts in terms of the resulting model's performance.
  arXiv  Detail & Related papers  (2023-06-28T03:31:31Z)
• M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios [103.6153593636399]
  We propose a vision-language prompt tuning method with mitigated label bias (M-Tuning) It introduces open words from the WordNet to extend the range of words forming the prompt texts from only closed-set label words to more, and thus prompts are tuned in a simulated open-set scenario. Our method achieves the best performance on datasets with various scales, and extensive ablation studies also validate its effectiveness.
  arXiv  Detail & Related papers  (2023-03-09T09:05:47Z)

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.