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Ming Sun

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12 papers

2 author rows

IJCAI Conference 2025 Conference Paper

Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling

Rui Qin
Qijie Wang
Ming Sun
Haowei Zhu
Chao Zhou
Bin Wang

Diffusion models have gained attention for their success in modeling complex distributions, achieving impressive perceptual quality in SR tasks. However, existing diffusion-based SR methods often suffer from high computational costs, requiring numerous iterative steps for training and inference. Existing acceleration techniques, such as distillation and solver optimization, are generally task-agnostic and do not fully leverage the specific characteristics of low-level tasks like super-resolution (SR). In this study, we analyze the frequency- and spatial-domain properties of diffusion-based SR methods, revealing key insights into the temporal and spatial dependencies of high-frequency signal recovery. Specifically, high-frequency details benefit from concentrated optimization during early and late diffusion iterations, while spatially textured regions demand adaptive denoising strategies. Building on these observations, we propose the Time-Spatial-aware Sampling strategy (TSS) for the acceleration of Diffusion SR without any extra training cost. TSS combines Time Dynamic Sampling (TDS), which allocates more iterations to refining textures, and Spatial Dynamic Sampling (SDS), which dynamically adjusts strategies based on image content. Extensive evaluations across multiple benchmarks demonstrate that TSS achieves state-of-the-art (SOTA) performance with significantly fewer iterations, improving MUSIQ scores by 0. 2~3. 0 and outperforming the current acceleration methods with only half the number of steps.

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AIIM Journal 2025 Journal Article

Anatomical prior-based vertebral landmark detection for spinal disorder diagnosis

Yukang Yang
Yu Wang
Tianyu Liu
Miao Wang
Ming Sun
Shiji Song
Wenhui Fan
Gao Huang

As one of fundamental ways to interpret spine images, detection of vertebral landmarks is an informative prerequisite for further diagnosis and management of spine disorders such as scoliosis and fractures. Most existing machine learning-based methods for automatic vertebral landmark detection suffer from overlapping landmarks or abnormally long distances between nearby landmarks against anatomical priors, and thus lack sufficient reliability and interpretability. To tackle the problem, this paper systematically utilizes anatomical prior knowledge in vertebral landmark detection. We explicitly formulate anatomical priors of the spine, related to distances among vertebrae and spatial order within the spine, and integrate these geometrical constraints within training loss, inference procedure, and evaluation metrics. First, we introduce an anatomy-constraint loss to regularize the training process with the aforementioned contextual priors explicitly. Second, we propose a simple-yet-effective anatomy-aided inference procedure by employing sequential prediction rather than a parallel counterpart. Third, we provide novel anatomy-related metrics to quantitatively evaluate to which extent landmark predictions follow the anatomical priors, as is not reflected within the widely-used landmark localization error metric. We employ the localization framework on 1410 anterior–posterior radiographic images. Compared with competitive baseline models, we achieve superior landmark localization accuracy and comparable Cobb angle estimation for scoliosis assessment. Ablation studies demonstrate the effectiveness of designed components on the decrease of localization error and improvement of anatomical plausibility. Additionally, we exhibit effective generalization performance by transferring our detection method onto sagittal 2-D slices of CT scans and boost the performance of downstream compression fracture classification at vertebra-level.

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AAAI Conference 2025 Conference Paper

Plug-and-Play Tri-Branch Invertible Block for Image Rescaling

Jingwei Bao
Jinhua Hao
Pengcheng Xu
Ming Sun
Chao Zhou
Shuyuan Zhu

High-resolution (HR) images are commonly downscaled to low-resolution (LR) to reduce bandwidth, followed by upscaling to restore their original details. Recent advancements in image rescaling algorithms have employed invertible neural networks (INNs) to create a unified framework for downscaling and upscaling, ensuring a one-to-one mapping between LR and HR images. Traditional methods, utilizing dual-branch based vanilla invertible blocks, process high-frequency and low-frequency information separately, often relying on specific distributions to model high-frequency components. However, processing the low-frequency component directly in the RGB domain introduces channel redundancy, limiting the efficiency of image reconstruction. To address these challenges, we propose a plug-and-play tri-branch invertible block (T-InvBlocks) that decomposes the low- frequency branch into luminance (Y) and chrominance (CbCr) components, reducing redundancy and enhancing feature processing. Additionally, we adopt an all-zero mapping strategy for high-frequency components during upscaling, focusing essential rescaling information within the LR image. Our T-InvBlocks can be seamlessly integrated into existing rescaling models, improving performance in both general rescaling tasks and scenarios involving lossy compression. Extensive experiments confirm that our method advances the state of the art in HR image reconstruction.

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IJCAI Conference 2025 Conference Paper

Preventing Latent Diffusion Model-Based Image Mimicry via Angle Shifting and Ensemble Learning

Minghao Li
Rui Wang
Ming Sun
Lihua Jing

The remarkable progress of Latent Diffusion Models (LDMs) in image generation has raised concerns about the potential for unauthorized image mimicry. To address these concerns, studies on adversarial attacks against LDMs have gained increasing attention in recent years. However, existing methods face bottlenecks when attacking the denoising module. In this work, we reveal that the robustness of the denoising module stems from two key factors: the cancellation effect between adversarial perturbations and estimated noise, and unstable gradients caused by randomly sampled timesteps and Gaussian noise. Based on these insights, we introduce a cosine similarity adversarial loss to prevent the generation of perturbations that are easily impaired and develop a more stable optimization strategy by ensembling gradients and fixing the noise in the latent space. Additionally, we propose an alternating iterative framework to reduce memory usage by mathematically dividing the optimization process into two spaces: latent space and pixel space. Compared to previous strategies, our proposed framework reduces video memory demands without sacrificing attack effectiveness. Extensive experiments demonstrate that the alternating iterative framework and the stable optimization strategy on cosine similarity loss are more efficient and more effective. Code is available at https: //github. com/MinghaoLi01/cosattack.

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ICLR Conference 2025 Conference Paper

Rethinking Diffusion Posterior Sampling: From Conditional Score Estimator to Maximizing a Posterior

Tongda Xu
Xiyan Cai
Xinjie Zhang
Xingtong Ge
Dailan He
Ming Sun
Jingjing Liu
Ya-Qin Zhang

Recent advancements in diffusion models have been leveraged to address inverse problems without additional training, and Diffusion Posterior Sampling (DPS) (Chung et al., 2022a) is among the most popular approaches. Previous analyses suggest that DPS accomplishes posterior sampling by approximating the conditional score. While in this paper, we demonstrate that the conditional score approximation employed by DPS is not as effective as previously assumed, but rather aligns more closely with the principle of maximizing a posterior (MAP). This assertion is substantiated through an examination of DPS on 512$\times$512 ImageNet images, revealing that: 1) DPS’s conditional score estimation significantly diverges from the score of a well-trained conditional diffusion model and is even inferior to the unconditional score; 2) The mean of DPS’s conditional score estimation deviates significantly from zero, rendering it an invalid score estimation; 3) DPS generates high-quality samples with significantly lower diversity. In light of the above findings, we posit that DPS more closely resembles MAP than a conditional score estimator, and accordingly propose the following enhancements to DPS: 1) we explicitly maximize the posterior through multi-step gradient ascent and projection; 2) we utilize a light-weighted conditional score estimator trained with only 100 images and 8 GPU hours. Extensive experimental results indicate that these proposed improvements significantly enhance DPS's performance. The source code for these improvements is provided in https://github.com/tongdaxu/Rethinking-Diffusion-Posterior-Sampling-From-Conditional-Score-Estimator-to-Maximizing-a-Posterior.

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NeurIPS Conference 2023 Conference Paper

Accelerating Monte Carlo Tree Search with Probability Tree State Abstraction

Yangqing Fu
Ming Sun
Buqing Nie
Yue Gao

Monte Carlo Tree Search (MCTS) algorithms such as AlphaGo and MuZero have achieved superhuman performance in many challenging tasks. However, the computational complexity of MCTS-based algorithms is influenced by the size of the search space. To address this issue, we propose a novel probability tree state abstraction (PTSA) algorithm to improve the search efficiency of MCTS. A general tree state abstraction with path transitivity is defined. In addition, the probability tree state abstraction is proposed for fewer mistakes during the aggregation step. Furthermore, the theoretical guarantees of the transitivity and aggregation error bound are justified. To evaluate the effectiveness of the PTSA algorithm, we integrate it with state-of-the-art MCTS-based algorithms, such as Sampled MuZero and Gumbel MuZero. Experimental results on different tasks demonstrate that our method can accelerate the training process of state-of-the-art algorithms with 10%-45% search space reduction.

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AIIM Journal 2022 Journal Article

A multi-scale keypoint estimation network with self-supervision for spinal curvature assessment of idiopathic scoliosis from the imperfect dataset

Tianyu Liu
Yu Wang
Yukang Yang
Ming Sun
Wenhui Fan
Cody Bunger
Cheng Wu

Idiopathic scoliosis (IS) is a common lifetime disease, which exhibits an obvious deformity of spinal curvature to seriously affect heart and lung function. Accurate radiographic assessment of spinal curvature is vitally important for the clinical diagnosis and treatment planning of idiopathic scoliosis. Deep learning algorithms have been widely adopted to the medical image analysis with the remarkable advancement in computer vision. The automated methods can improve the efficiency of clinical diagnosis to relieve the burden of doctors, which have advantage in dealing with the tedious and repetitive tasks. However, existing methods usually require sufficiently large training datasets with strict annotation, which are costly and laborious especially for medical images. Moreover, the medical images of serious IS always contain the blurry and occlusive parts, which would make the accurate and robust estimation of the spinal curvature more difficult. In this paper, a dot annotation approach is presented to train the spinal curvature assessment model, rather than using strict annotation of IS X-ray images. We develop a multi-scale keypoint estimation network to reduce the requirement for large training datasets, in which the Squeeze-and-Excitation (SE) blocks are incorporated to improve the representational capacity of the model. Then, a self-supervision module is designed to alleviate the blurry and occlusive problem, and we use the two-view radiographic assessments of IS to generate a 3D spinal curvature. Finally, extensive experiments are conducted on a collected clinical dataset, in which we obtain 81. 5 AP and the average E d between the predicted keypoints and the ground truths is 0. 43, making an improvement over the mainstream approaches.

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NeurIPS Conference 2020 Conference Paper

Improving Auto-Augment via Augmentation-Wise Weight Sharing

Keyu Tian
Chen Lin
Ming Sun
Luping Zhou
Junjie Yan
Wanli Ouyang

The recent progress on automatically searching augmentation policies has boosted the performance substantially for various tasks. A key component of automatic augmentation search is the evaluation process for a particular augmentation policy, which is utilized to return reward and usually runs thousands of times. A plain evaluation process, which includes full model training and validation, would be time-consuming. To achieve efficiency, many choose to sacrifice evaluation reliability for speed. In this paper, we dive into the dynamics of augmented training of the model. This inspires us to design a powerful and efficient proxy task based on the Augmentation-Wise Weight Sharing (AWS) to form a fast yet accurate evaluation process in an elegant way. Comprehensive analysis verifies the superiority of this approach in terms of effectiveness and efficiency. The augmentation policies found by our method achieve superior accuracies compared with existing auto-augmentation search methods. On CIFAR-10, we achieve a top-1 error rate of 1. 24%, which is currently the best performing single model without extra training data. On ImageNet, we get a top-1 error rate of 20. 36% for ResNet-50, which leads to 3. 34% absolute error rate reduction over the baseline augmentation.

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NeurIPS Conference 2019 Conference Paper

Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection

Junran Peng
Ming Sun
ZHAO-XIANG ZHANG
Tieniu Tan
Junjie Yan

Recently, Neural Architecture Search has achieved great success in large-scale image classification. In contrast, there have been limited works focusing on architecture search for object detection, mainly because the costly ImageNet pretraining is always required for detectors. Training from scratch, as a substitute, demands more epochs to converge and brings no computation saving. To overcome this obstacle, we introduce a practical neural architecture transformation search(NATS) algorithm for object detection in this paper. Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights. We propose a novel neural architecture search strategy in channel-level instead of path-level and devise a search space specially targeting at object detection. With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection. Since our method is gradient-based and only searches for a transformation scheme, the weights of models pretrained in ImageNet could be utilized in both searching and retraining stage, which makes the whole process very efficient. The transformed network requires no extra parameters and FLOPs, and is friendly to hardware optimization, which is practical to use in real-time application. In experiments, we demonstrate the effectiveness of NATS on networks like {\em ResNet} and {\em ResNeXt}. Our transformed networks, combined with various detection frameworks, achieve significant improvements on the COCO dataset while keeping fast.

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NeurIPS Conference 2018 Conference Paper

Compact Generalized Non-local Network

Kaiyu Yue
Ming Sun
Yuchen Yuan
Feng Zhou
Errui Ding
Fuxin Xu

The non-local module is designed for capturing long-range spatio-temporal dependencies in images and videos. Although having shown excellent performance, it lacks the mechanism to model the interactions between positions across channels, which are of vital importance in recognizing fine-grained objects and actions. To address this limitation, we generalize the non-local module and take the correlations between the positions of any two channels into account. This extension utilizes the compact representation for multiple kernel functions with Taylor expansion that makes the generalized non-local module in a fast and low-complexity computation flow. Moreover, we implement our generalized non-local method within channel groups to ease the optimization. Experimental results illustrate the clear-cut improvements and practical applicability of the generalized non-local module on both fine-grained object recognition and video classification. Code is available at: https: //github. com/KaiyuYue/cgnl-network. pytorch.

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IJCAI Conference 2017 Conference Paper

Joint Image Emotion Classification and Distribution Learning via Deep Convolutional Neural Network

Jufeng Yang
Dongyu She
Ming Sun

Visual sentiment analysis is attracting more and more attention with the increasing tendency to express emotions through visual contents. Recent algorithms in convolutional neural networks (CNNs) considerably advance the emotion classification, which aims to distinguish differences among emotional categories and assigns a single dominant label to each image. However, the task is inherently ambiguous since an image usually evokes multiple emotions and its annotation varies from person to person. In this work, we address the problem via label distribution learning (LDL) and develop a multi-task deep framework by jointly optimizing both classification and distribution prediction. While the proposed method prefers to the distribution dataset with annotations of different voters, the majority voting scheme is widely adopted as the ground truth in this area, and few dataset has provided multiple affective labels. Hence, we further exploit two weak forms of prior knowledge, which are expressed as similarity information between labels, to generate emotional distribution for each category. The experiments conducted on both distribution datasets, i. e. , Emotion6, Flickr_LDL, Twitter_LDL, and the largest single emotion dataset, i. e. , Flickr and Instagram, demonstrate the proposed method outperforms the state-of-the-art approaches.

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AAAI Conference 2017 Conference Paper

Learning Visual Sentiment Distributions via Augmented Conditional Probability Neural Network

Jufeng Yang
Ming Sun
Xiaoxiao Sun

Visual sentiment analysis is raising more and more attention with the increasing tendency to express emotions through images. While most existing works assign a single dominant emotion to each image, we address the sentiment ambiguity by label distribution learning (LDL), which is motivated by the fact that image usually evokes multiple emotions. Two new algorithms are developed based on conditional probability neural network (CPNN). First, we propose BCPNN which encodes image label into a binary representation to replace the signless integers used in CPNN, and employ it as a part of input for the neural network. Then, we train our ACPNN model by adding noises to ground truth label and augmenting affective distributions. Since current datasets are mostly annotated for single-label learning, we build two new datasets, one of which is relabeled on the popular Flickr dataset and the other is collected from Twitter. These datasets contain 20, 745 images with multiple affective labels, which are over ten times larger than the existing ones. Experimental results show that the proposed methods outperform the state-of-theart works on our large-scale datasets and other publicly available benchmarks.

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