Author name cluster

Zhekai Du

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

2 author rows

AAAI Conference 2026 Conference Paper

Generalizing Vision-Language Models with Dedicated Prompt Guidance

Xinyao Li
Yinjie Min
Hongbo Chen
Zhekai Du
Fengling Li
Jingjing Li

Fine-tuning large pretrained vision-language models (VLMs) has emerged as a prevalent paradigm for downstream adaptation, yet it faces a critical trade-off between domain specificity and domain generalization (DG) ability. Current methods typically fine-tune a universal model on the entire dataset, which potentially compromises the ability to generalize to unseen domains. To fill this gap, we provide a theoretical understanding of the generalization ability for VLM fine-tuning, which reveals that training multiple parameter-efficient expert models on partitioned source domains leads to better generalization than fine-tuning a universal model. Inspired by this finding, we propose a two-step domain-expert-Guided DG (GuiDG) framework. GuiDG first employs prompt tuning to obtain source domain experts, then introduces a Cross-Modal Attention module to guide the fine-tuning of the vision encoder via adaptive expert integration. To better evaluate few-shot DG, we construct ImageNet-DG from ImageNet and its variants. Extensive experiments on standard DG benchmarks and ImageNet-DG demonstrate that GuiDG improves upon state-of-the-art fine-tuning methods while maintaining efficiency.

PDF Details DOI

JBHI Journal 2025 Journal Article

Fourier-Based Frequency Space Disentanglement and Augmentation for Generalizable Face Anti-Spoofing

Yang Yu
Zhekai Du
Heng Luo
Chengwei Xiao
Jiang Hu

Generalizing face anti-spoofing (FAS) models to unseen distributions is challenging due to domain shifts. Previous domain generalization (DG) based FAS methods focus on learning invariant features across domains in the spatial space, which may be ineffective in detecting subtle spoof patterns. In this paper, we propose a novel approach called Frequency Space Disentanglement and Augmentation (FSDA) for generalizable FAS. Specifically, we leverage Fourier transformation to analyze face images in the frequency space, where the amplitude spectrum captures low-level texture information that forms distinct visual appearances, and the phase spectrum corresponds to the content information. We hypothesize that the liveness of a face is more related to these low-level patterns rather than high-level content information. To locate spoof traces, we disentangle the amplitude spectrum into domain-related and spoof-related components using either empirical or learnable strategies. We then propose a frequency space augmentation technique that mixes the disentangled components of two images to synthesize new variations. By imposing a distillation loss and a consistency loss on the augmented samples, our model learns to capture spoof patterns that are robust to both domain and spoof type variations. Extensive experiments on four FAS datasets demonstrate the superiority of our method in improving the generalization ability of FAS models in various unseen scenarios.

Details DOI

ICLR Conference 2025 Conference Paper

LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient Fine-Tuning

Zhekai Du
Yinjie Min
Jingjing Li 0001
Ke Lu 0001
Changliang Zou
Liuhua Peng
Tingjin Chu
Mingming Gong

Low-rank adaptation (LoRA) has become a prevalent method for adapting pre-trained large language models to downstream tasks. However, the simple low-rank decomposition form may constrain the optimization flexibility. To address this limitation, we introduce Location-aware Cosine Adaptation (LoCA), a novel frequency-domain parameter-efficient fine-tuning method based on inverse Discrete Cosine Transform (iDCT) with selective locations of learnable components. We begin with a comprehensive theoretical comparison between frequency-domain and low-rank decompositions for fine-tuning pre-trained large models. Our analysis reveals that frequency-domain decomposition with carefully selected frequency components can surpass the expressivity of traditional low-rank-based methods. Furthermore, we demonstrate that iDCT offers a more efficient implementation compared to inverse Discrete Fourier Transform (iDFT), allowing for better selection and tuning of frequency components while maintaining equivalent expressivity to the optimal iDFT-based adaptation. By employing finite-difference approximation to estimate gradients for discrete locations of learnable coefficients on the DCT spectrum, LoCA dynamically selects the most informative frequency components during training. Experiments on diverse language and vision fine-tuning tasks demonstrate that LoCA offers enhanced parameter efficiency while maintains computational feasibility comparable to low-rank-based methods.

Details

AAAI Conference 2023 Conference Paper

Cross-Domain Adaptative Learning for Online Advertisement Customer Lifetime Value Prediction

Hongzu Su
Zhekai Du
Jingjing Li
Lei Zhu
Ke Lu

Accurate estimation of customer lifetime value (LTV), which reflects the potential consumption of a user over a period of time, is crucial for the revenue management of online advertising platforms. However, predicting LTV in real-world applications is not an easy task since the user consumption data is usually insufficient within a specific domain. To tackle this problem, we propose a novel cross-domain adaptative framework (CDAF) to leverage consumption data from different domains. The proposed method is able to simultaneously mitigate the data scarce problem and the distribution gap problem caused by data from different domains. To be specific, our method firstly learns a LTV prediction model from a different but related platform with sufficient data provision. Subsequently, we exploit domain-invariant information to mitigate data scarce problem by minimizing the Wasserstein discrepancy between the encoded user representations of two domains. In addition, we design a dual-predictor schema which not only enhances domain-invariant information in the semantic space but also preserves domain-specific information for accurate target prediction. The proposed framework is evaluated on five datasets collected from real historical data on the advertising platform of Tencent Games. Experimental results verify that the proposed framework is able to significantly improve the LTV prediction performance on this platform. For instance, our method can boost DCNv2 with the improvement of 13.7% in terms of AUC on dataset G2. Code: https://github.com/TL-UESTC/CDAF.

PDF Details DOI

NeurIPS Conference 2023 Conference Paper

Diffusion-Based Probabilistic Uncertainty Estimation for Active Domain Adaptation

Zhekai Du
Jingjing Li

Active Domain Adaptation (ADA) has emerged as an attractive technique for assisting domain adaptation by actively annotating a small subset of target samples. Most ADA methods focus on measuring the target representativeness beyond traditional active learning criteria to handle the domain shift problem, while leaving the uncertainty estimation to be performed by an uncalibrated deterministic model. In this work, we introduce a probabilistic framework that captures both data-level and prediction-level uncertainties beyond a point estimate. Specifically, we use variational inference to approximate the joint posterior distribution of latent representation and model prediction. The variational objective of labeled data can be formulated by a variational autoencoder and a latent diffusion classifier, and the objective of unlabeled data can be implemented in a knowledge distillation framework. We utilize adversarial learning to ensure an invariant latent space. The resulting diffusion classifier enables efficient sampling of all possible predictions for each individual to recover the predictive distribution. We then leverage a t-test-based criterion upon the sampling and select informative unlabeled target samples based on the p-value, which encodes both prediction variability and cross-category ambiguity. Experiments on both ADA and Source-Free ADA settings show that our method provides more calibrated predictions than previous ADA methods and achieves favorable performance on three domain adaptation datasets.

PDF Details

EAAI Journal 2021 Journal Article

Local–Global Attentive Adaptation for Object Detection

Dan Zhang
Jingjing Li
Xingpeng Li
Zhekai Du
Lin Xiong
Mao Ye

Adversarial adaptive methods have been proven to be useful for domain transfer in many fields such as image recognition and semantic segmentation, etc However, for object detection, since each image could have different combinations of objects, brutally aligning all the images without considering their transferability may cause the notorious phenomena named ‘negative transfer’. On the other hand, strong matching the local-level features makes sense, as it not only reduces the discrepancy between different domain distributions, but preserves the category-level semantic information. However, it is hard to markedly achieve domain invariance using a simple adversarial adaptive method. In this work, we propose an effective method termed Local–Global Attentive Adaptation for object Detection (LGAAD). Our method can alleviate the negative transfer caused by improper global alignments through leveraging an adaptively and dynamically weighted transferability to highlight the more transferable images. Furthermore, the proposed method also achieves the strong matching between two domains at local-level features to alleviate the cross-domain discrepancy by using the attention mechanism after multiple local discriminators. Additionally, we also consider the domain impacts of instance-wise features and backgrounds in images with large domain divergence, a non-negligible factor for improving the domain adaptive detection model performance. Extensive experiments of various domain shift scenarios show that our method exceeds the state-of-the-art results on several public datasets. Furthermore, qualitative visualization and ablation analyzes can demonstrate the validity of our approach for attending the interested regions and instances on domain adaptation.

Details DOI