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Kangping Wang

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AAAI Conference 2025 Conference Paper

WST: Wavelet-Based Multi-scale Tuning for Visual Transfer Learning

Jia Zeng
Lan Huang
Kangping Wang

Large-scale pre-trained Vision Transformer (ViT) models have demonstrated remarkable performance on visual tasks but are computationally expensive to transfer to downstream tasks. Parameter-Efficient Fine-Tuning (PEFT) offers a promising transferring approach by updating only a subset of parameters. However, PEFT's effectiveness is hindered by discrepancies between pre-training and downstream tasks in terms of object scale and granularity. Downstream tasks often focus on finer-grained and more specialized recognition, requiring more detailed features. The diversity of feature scales of existing PEFT methods for ViT is limited. To address this, we propose a novel PEFT method named Wavelet-based multi-Scale Tuning (WST), which learns multi-scale features in a simple and efficient way. WST introduces a parallel fine-tuning patch embedding branch with a smaller patch size than the pre-trained model to capture finer-grained features. Furthermore, to handle the computational challenge from the resulting longer token sequence, WST designs wavelet fine-tuning blocks that balance both efficiency and performance. In the block, wavelet transform enables invertible and lossless down-sampling of the longer token sequence, aligning it with that of the backbone, and two lightweight linear mappings are employed to learn task-specific features. This design facilitates efficient multi-scale information exchange between the pre-trained backbone and fine-tuning branch. Extensive experiments on transfer learning demonstrate the promising performance and efficiency of our WST.

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EAAI Journal 2024 Journal Article

Prompt-based learning framework for zero-shot cross-lingual text classification

Kai Feng
Lan Huang
Kangping Wang
Wei Wei
Rui Zhang

Cross-lingual text classification is a challenging task that aims to train classifiers with data in one language, known as the source language, and apply the acquired knowledge to data in another language, referred to as the target language. Recent advancements in multilingual pre-trained language models (PLMs) have made significant progress in addressing cross-lingual issues, and the application of prompt-based learning has further improved task performance. However, these models still face challenges such as the gap between cross-lingual classification tasks and pre-training tasks of PLMs, as well as issues related to scarce resources and data noise, which hinder the full exploitation of the implicit knowledge in PLMs. In this paper, we propose a Prompt-based Cross-lingual Learning (PCL) framework that combines language-agnostic continuous prompt learning with self-learning process. Specifically, PCL framework leverages language-agnostic prompts and PLMs to achieve semantic transfer between source and target languages. To enhance the semantic relationship between prompts and category labels, a label attention module is introduced. Additionally, a set of self-training rules is proposed, which includes a scoring function. In a few-shot setting, noisy data is dynamically filtered through scoring and ranking of the data. During each training iteration, both the model and scoring function weights are updated, further improving the discrimination capability of the model. In summary, the proposed PCL framework builds upon cross-lingual prompt learning, effectively removing noisy data and applying it to zero-shot cross-lingual text classification, which is beneficial for engineering applications. The findings of this study have implications for prompt learning method. The PCL framework achieves state-of-the-art performance in cross-lingual text classification task, with a 14% performance improvement compared to basic soft prompt learning. This demonstrates its potential in addressing classification problems in resource-limited scenarios.

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