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Qin Shi

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NeurIPS Conference 2025 Conference Paper

Knowledge Distillation Detection for Open-weights Models

Qin Shi
Amber Yijia Zheng
Qifan Song
Raymond A. Yeh

We propose the task of knowledge distillation detection, which aims to determine whether a student model has been distilled from a given teacher, under a practical setting where only the student’s weights and the teacher’s API are available. This problem is motivated by growing concerns about model provenance and unauthorized replication through distillation. To address this task, we introduce a model-agnostic framework that combines data-free input synthesis and statistical score computation for detecting distillation. Our approach is applicable to both classification and generative models. Experiments on diverse architectures for image classification and text-to-image generation show that our method improves detection accuracy over the strongest baselines by 59. 6\% on CIFAR-10, 71. 2\% on ImageNet, and 20. 0\% for text-to-image generation. The code is available at https: //github. com/shqii1j/distillation_detection.

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EAAI Journal 2023 Journal Article

Perceiving Multiple Representations for scene text image super-resolution guided by text recognizer

Qin Shi
Yu Zhu
Yatong Liu
Jiongyao Ye
Dawei Yang

Single image super-resolution (SISR) aims to recover clear high-resolution images from low-resolution images, which has made great progress with the development of deep learning these years. Scene text image super-resolution (STISR) is a subfield of SISR with the goal of increasing the resolution of a low-resolution text image and enhancing the readability of characters in the image. Despite significant improvements in recent approaches, STISR remains a challenging task due to the diversity of background, text appearances and layouts, etc. This paper presents a Perceiving Multiple Representations (PerMR) method for better super-resolution performances in scene text images. PerMR is a unified network that combines super-resolution with text recognition and exploits the recognizer’s feedback to facilitate super-resolution. Specifically, contextual information from the text decoder is extracted to provide sequence-specific guidance and enable the super-resolution model to pay more attention to the text region. Meanwhile, low-level and high-level visual features from the vision backbone of the recognition network are integrated to further improve visual quality. Additionally, we incorporate a frequency branch into the vanilla convolution unit, which efficiently enhances global and local feature representations. Experiments on the STISR benchmark dataset TextZoom validate that PerMR can not only generate more distinguishable images, but also outperforms the current state-of-the-art methods. PerMR boosts the average recognition accuracy by 5. 9% using ASTER, 5. 8% using MORAN and 10. 6% using CRNN compared to the baseline model TSRN. PerMR outperforms the advanced method TPGSR-3 by 1. 4% on ASTER, 0. 1% on MORAN, 0. 2% on CRNN and boosts TATT by 0. 6% on ASTER and 1. 1% on MORAN respectively. Furthermore, PerMR demonstrates good robustness and generalization when tackling low-quality text images in multiple scene text recognition datasets. The experiment results verify the capabilities of PerMR to boost text recognition performance.

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NeurIPS Conference 2023 Conference Paper

Sequential Subset Matching for Dataset Distillation

Jiawei Du
Qin Shi
Joey Tianyi Zhou

Dataset distillation is a newly emerging task that synthesizes a small-size dataset used in training deep neural networks (DNNs) for reducing data storage and model training costs. The synthetic datasets are expected to capture the essence of the knowledge contained in real-world datasets such that the former yields a similar performance as the latter. Recent advancements in distillation methods have produced notable improvements in generating synthetic datasets. However, current state-of-the-art methods treat the entire synthetic dataset as a unified entity and optimize each synthetic instance equally. This static optimization approach may lead to performance degradation in dataset distillation. Specifically, we argue that static optimization can give rise to a coupling issue within the synthetic data, particularly when a larger amount of synthetic data is being optimized. This coupling issue, in turn, leads to the failure of the distilled dataset to extract the high-level features learned by the deep neural network (DNN) in the latter epochs. In this study, we propose a new dataset distillation strategy called Sequential Subset Matching (SeqMatch), which tackles this problem by adaptively optimizing the synthetic data to encourage sequential acquisition of knowledge during dataset distillation. Our analysis indicates that SeqMatch effectively addresses the coupling issue by sequentially generating the synthetic instances, thereby enhancing its performance significantly. Our proposed SeqMatch outperforms state-of-the-art methods in various datasets, including SVNH, CIFAR-10, CIFAR-100, and Tiny ImageNet.

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