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Anlong Ming

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18 papers

2 author rows

EAAI Journal 2026 Journal Article

Direction-aware deep policy learning for efficient capacitated arc routing

Feng Xue
Runze Guo
Anlong Ming
Nicu Sebe

Line coverage of urban-scale routes by multiple vehicles can be formulated as the Capacitated Arc Routing Problem (CARP). Early solutions, including constructive heuristics, meta-heuristics, and exact algorithms, struggle to achieve efficient runtimes. Recently, neural network (NN)-based methods have emerged as promising alternatives for efficiently solving CARP; however, their solution quality significantly lags behind non-NN approaches. This significant gap mainly results from the inappropriate modeling and learning of non-Euclidean graphs, traversal direction and capacity constraints. In this paper, we introduce an NN-based solver tailored for these complexities, which significantly narrows the gap with advanced meta-heuristics while achieving much shorter runtimes. First, we propose the direction-aware attention model (DaAM) to incorporate directionality into the embedding process, facilitating more effective one-stage decision-making. Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy for subsequent reinforcement fine-tuning. It proves particularly valuable for solving CARP that has a higher complexity than the node routing problems (NRPs). Finally, a path optimization method is introduced to adjust the depot return positions within the path generated by DaAM. Experiments show that DaAM surpasses constructive heuristics and achieves decision quality comparable to state-of-the-art meta-heuristics for the first time while maintaining superior efficiency, even in large-scale CARP instances. The code and datasets are provided in the link and will be released on GitHub.

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EAAI Journal 2026 Journal Article

Exercise quality assessment in monocular video streaming

Yongchang Zhang
Boxuan Xu
Zhaowen Lin
Junjie Li
Anlong Ming

The recent proliferation of home-based exercise content has garnered significant attention. This has led to an increasing demand for Artificial Intelligence (AI) devices capable of automatically assessing exercise quality and providing guidance. However, existing real-time exercise quality assessment algorithms require instructors and learners to share similar camera views. Furthermore, these methods often rely on pre-labeled data, support a limited number of exercise actions, and offer restricted feedback. Among the numerous videos where data is hard to pre-label, instructors and learners may have unrestricted camera views and inconsistent body shape, while instructors may demonstrate unpredicted actions. To address the aforementioned challenges, we propose a method for Exercise Quality Assessment in Monocular Videos (MV-EQA), which incorporates a Skeleton Mapping and View Aligning (SMVA) module, a Multi-Feature Dynamic Time Warping (MF-DTW) module, and online/offline Exercise Quality Assessment (EQA) modules. Specifically, SMVA utilizes a lightweight encoder–decoder network based on transformer architecture that effectively handles differences in view and skeleton between learners and instructors while preserving inherent variations in their movements; MF-DTW utilizes multiple body information for temporal alignment; online/offline EQA modules enable online feedback (scoring with visual comparison) and offline feedback (reviews with comments). Extensive experiments indicate the superiority of our approach over other methods in EQA tasks. The code is available at link.

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AAAI Conference 2026 Conference Paper

Regression over Classification: Assessing Image Aesthetics via Multimodal Large Language Models

Xingyuan Ma
Shuai He
Anlong Ming
Haobin Zhong
Huadong Ma

Image Aesthetics Assessment (IAA) evaluates visual quality through user-centered perceptual analysis and can guide various applications. Recent advances in Multimodal Large Language Models (MLLMs) have sparked interest in adapting them for IAA. However, two critical limitations persist in applying MLLMs to IAA: 1) the tokenization strategy leads to insensitivity to scores, and 2) the classification-based decoding mechanisms introduce score quantization errors. Current MLLM-based IAA methods treat the task as coarse rating classification followed by probability-to-score mapping, which loses fine-grained information. To address these challenges, we propose ROC4MLLM, offering complementary solutions from two perspectives:1) Representation: We separate scores from the word token space to avoid tokenizing scores as text. An independent position token bridges these spaces, improving the sensitivity of the model to score positions in text. 2) Computation: We apply distinct loss functions for text and score predictions to enhance the sensitivity of the model to score gradients. Decoupling scores from text ensures effective supervision while preventing interference between scores and text in the loss computation. Extensive experiments across five datasets demonstrate that ROC4MLLM achieves state-of-the-art performance without requiring additional training data. Additionally, its plug-and-play design ensures seamless integration with existing MLLMs, boosting their IAA performance.

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AAAI Conference 2026 Conference Paper

Thinking Aesthetics Assessment of Image Color Temperature: Models, Datasets and Benchmarks

Jinguang Cheng
Chunxiao Li
Shuai He
Taiyu Chen
Anlong Ming

Color temperature, as a crucial attribute influencing image color, plays a critical role in Image Aesthetics Assessment (IAA). Yet, within the existing IAA field, little light has been shed on assessing the aesthetic quality of image color temperature. To bridge this gap, we introduce a new task: Image Color Temperature Aesthetics Assessment (ICTAA). However, this task poses the following challenges: 1) Perceptual Sensitivity: humans exhibit high sensitivity to subtle shifts in color temperature, necessitating a model to enable fine-grained discrimination; 2) Spectral Continuity: The theoretical modeling of color temperature aesthetics requires continuous labels; however, the just-noticeable-difference property of human perception makes continuous labeling infeasible, necessitating a well-designed labeling strategy. To address the aforementioned challenges, we make the following efforts. First, we propose a multi-modal contrastive learning framework, ICTA2Net, that models color temperature differences between image pairs while strictly controlling other visual attributes. Second, leveraging color temperature transitivity, we design a weakly supervised strategy that discretely samples images based on anchor images and human perception to build contrastive relations across color temperatures, enabling learning from discrete labels. Thirdly, we construct a color temperature aesthetics dataset, ICTAA240K, and a benchmark for validation. Additionally, we propose a new metric, Information Entropy-weighted Accuracy (IEA), which weights accuracy by the degree of annotation disagreement to reflect model performance across varying sample difficulties, complementing existing evaluation metrics. Experiments show our method outperforms existing state-of-the-art IAA methods on ICTAA240K, thereby setting an effective roadmap for ICTAA.

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IJCAI Conference 2024 Conference Paper

AK4Prompts: Aesthetics-driven Automatically Keywords-Ranking for Prompts in Text-To-Image Models

Haiyang Zhang
Mengchao Wang
Shuai He
Anlong Ming

Current text-to-image synthesis (TIS) models have demonstrated the ability to generate high-fidelity images based on textual prompts. However, the efficacy of these models heavily relies on the keywords present in the prompts, and there is a dearth of objective analysis regarding how different keywords impact the ultimate quality of generated results. Therefore, manual evaluation becomes necessary but limited and inefficient to ascertain the role played by keywords. In this paper, we propose automated keywords-ranking for prompts (AK4Prompts), a keyword evaluation model based on mainstream TIS models that explicitly quantifies the multidimensional impact of various keywords on image generation based on prompts. To enable personalized keyword evaluation based on prompt content, we propose decoupling the latent representations of keywords and prompts in TIS models, followed by integrating the semantic features of prompts into keywords. For quantitative and multidimensional evaluation, we align the fused features of keywords using HPSv2, aesthetic score, and CLIP score, each representing distinct factors contributing to keyword impact. Our AK4Prompts can flexibly and automatically select the keywords that best match the original prompt based on individual user preferences. Extensive experimental results show the superiority of AK4Prompts to improve the quality of generated images significantly over strong baselines. Our approach not only enhances usability and user experience but also addresses the current gap in automated analysis and evaluation of keyword effects. Our code is availableat https: //github. com/mRobotit/AK4Prompts.

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ICML Conference 2024 Conference Paper

ELTA: An Enhancer against Long-Tail for Aesthetics-oriented Models

Limin Liu
Shuai He
Anlong Ming
Rui Xie
Huadong Ma

Real-world datasets often exhibit long-tailed distributions, compromising the generalization and fairness of learning-based models. This issue is particularly pronounced in Image Aesthetics Assessment (IAA) tasks, where such imbalance is difficult to mitigate due to a severe distribution mismatch between features and labels, as well as the great sensitivity of aesthetics to image variations. To address these issues, we propose an Enhancer against Long-Tail for Aesthetics-oriented models (ELTA). ELTA first utilizes a dedicated mixup technique to enhance minority feature representation in high-level space while preserving their intrinsic aesthetic qualities. Next, it aligns features and labels through a similarity consistency approach, effectively alleviating the distribution mismatch. Finally, ELTA adopts a specific strategy to refine the output distribution, thereby enhancing the quality of pseudo-labels. Experiments on four representative datasets (AVA, AADB, TAD66K, and PARA) show that our proposed ELTA achieves state-of-the-art performance by effectively mitigating the long-tailed issue in IAA datasets. Moreover, ELTA is designed with plug-and-play capabilities for seamless integration with existing methods. To our knowledge, this is the first contribution in the IAA community addressing long-tail. All resources are available in here.

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IJCAI Conference 2024 Conference Paper

M2Beats: When Motion Meets Beats in Short-form Videos

Dongxiang Jiang
Yongchang Zhang
Shuai He
Anlong Ming

In recent years, short-form videos have gained popularity and the editing of these videos, particularly when motion is synchronized with music, is highly favored due to its beat-matching effect. However, detecting motion rhythm poses a significant challenge as it is influenced by multiple factors that make it difficult to define using explicit rules. While traditional methods attempt to define motion rhythm, they often yield unsatisfactory results. On the other hand, learning-based methods can extract motion rhythm without relying on explicit rules but require high-quality datasets. Unfortunately, existing datasets simply substitute music rhythm for motion rhythm which are not equivalent. To address these challenges, we present the motion rhythm dataset AIST-M2B, which is annotated with meticulously curated motion rhythm labels derived from the profound correlation between motion and music in professional dance. We propose a novel network architecture called M2BNet that is specifically trained on AIST-M2B to effectively extract intricate motion rhythms by incorporating both human body structure and temporal information. Additionally, we introduce a pioneering algorithm for enhancing motion rhythm synchronization with beats. Experimental results substan- tiate the superior performance of our method compared to other existing algorithms in the domain of motion rhythm analysis. Our code is available at https: //github. com/mRobotit/M2Beats.

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NeurIPS Conference 2024 Conference Paper

Rethinking No-reference Image Exposure Assessment from Holism to Pixel: Models, Datasets and Benchmarks

Shuai He
Shuntian Zheng
Anlong Ming
Banyu Wu
Huadong Ma

The past decade has witnessed an increasing demand for enhancing image quality through exposure, and as a crucial prerequisite in this endeavor, Image Exposure Assessment (IEA) is now being accorded serious attention. However, IEA encounters two persistent challenges that remain unresolved over the long term: the accuracy and generalizability of No-reference IEA are inadequate for practical applications; the scope of IEA is confined to qualitative and quantitative analysis of the entire image or subimage, such as providing only a score to evaluate the exposure level, thereby lacking intuitive and precise fine-grained evaluation for complex exposure conditions. The objective of this paper is to address the persistent bottleneck challenges from three perspectives: model, dataset, and benchmark. 1) Model-level: we propose a Pixel-level IEA Network (P-IEANet) that utilizes Haar discrete wavelet transform (DWT) to analyze, decompose, and assess exposure from both lightness and structural perspectives, capable of generating pixel-level assessment results under no-reference scenarios. 2) Dataset-level: we elaborately build an exposure-oriented dataset, IEA40K, containing 40K images, covering 17 typical lighting scenarios, 27 devices, and 50+ scenes, with each image densely annotated by more than 10 experts with pixel-level labels. 3) Benchmark-level: we develop a comprehensive benchmark of 19 methods based on IEA40K. Our P-IEANet not only achieves state-of-the-art (SOTA) performance on all metrics but also seamlessly integrates with existing exposure correction and lighting enhancement methods. To our knowledge, this is the first work that explicitly emphasizes assessing complex image exposure problems at a pixel level, providing a significant boost to the IEA and exposure-related community. The code and dataset are available in \href{https: //github. com/mRobotit/Pixel-level-No-reference-Image-Exposure-Assessment}{\textcolor{red} {here}}.

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IJCAI Conference 2023 Conference Paper

ICDA: Illumination-Coupled Domain Adaptation Framework for Unsupervised Nighttime Semantic Segmentation

Chenghao Dong
Xuejing Kang
Anlong Ming

The performance of nighttime semantic segmentation has been significantly improved thanks to recent unsupervised methods. However, these methods still suffer from complex domain gaps, i. e. , the challenging illumination gap and the inherent dataset gap. In this paper, we propose the illumination-coupled domain adaptation framework(ICDA) to effectively avoid the illumination gap and mitigate the dataset gap by coupling daytime and nighttime images as a whole with semantic relevance. Specifically, we first design a new composite enhancement method(CEM) that considers not only illumination but also spatial consistency to construct the source and target domain pairs, which provides the basic adaptation unit for our ICDA. Next, to avoid the illumination gap, we devise the Deformable Attention Relevance(DAR) module to capture the semantic relevance inside each domain pair, which can couple the daytime and nighttime images at the feature level and adaptively guide the predictions of nighttime images. Besides, to mitigate the dataset gap and acquire domain-invariant semantic relevance, we propose the Prototype-based Class Alignment(PCA) module, which improves the usage of category information and performs fine-grained alignment. Extensive experiments show that our method reduces the complex domain gaps and achieves state-of-the-art performance for nighttime semantic segmentation. Our code is available at https: //github. com/chenghaoDong666/ICDA.

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AAAI Conference 2023 Conference Paper

SWBNet: A Stable White Balance Network for sRGB Images

Chunxiao Li
Xuejing Kang
Zhifeng Zhang
Anlong Ming

The white balance methods for sRGB images (sRGB-WB) aim to directly remove their color temperature shifts. Despite achieving promising white balance (WB) performance, the existing methods suffer from WB instability, i.e., their results are inconsistent for images with different color temperatures. We propose a stable white balance network (SWBNet) to alleviate this problem. It learns the color temperature-insensitive features to generate white-balanced images, resulting in consistent WB results. Specifically, the color temperatureinsensitive features are learned by implicitly suppressing lowfrequency information sensitive to color temperatures. Then, a color temperature contrastive loss is introduced to facilitate the most information shared among features of the same scene and different color temperatures. This way, features from the same scene are more insensitive to color temperatures regardless of the inputs. We also present a color temperature sensitivity-oriented transformer that globally perceives multiple color temperature shifts within an image and corrects them by different weights. It helps to improve the accuracy of stabilized SWBNet, especially for multiillumination sRGB images. Experiments indicate that our SWBNet achieves stable and remarkable WB performance.

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IJCAI Conference 2023 Conference Paper

WBFlow: Few-shot White Balance for sRGB Images via Reversible Neural Flows

Chunxiao Li
Xuejing Kang
Anlong Ming

The sRGB white balance methods aim to correct the nonlinear color cast of sRGB images without accessing raw values. Although existing methods have achieved increasingly better results, their generalization to sRGB images from multiple cameras is still under explored. In this paper, we propose the network named WBFlow that not only performs superior white balance for sRGB images but also generalizes well to multiple cameras. Specifically, we take advantage of neural flow to ensure the reversibility of WBFlow, which enables lossless rendering of color cast sRGB images back to pseudo raw features for linear white balancing and thus achieves superior performance. Furthermore, inspired by camera transformation approaches, we have designed a camera transformation (CT) in pseudo raw feature space to generalize WBFlow for different cameras via few shot learning. By utilizing a few sRGB images from an untrained camera, our WBFlow can perform well on this camera by learning the camera specific parameters of CT. Extensive experiments show that WBFlow achieves superior camera generalization and accuracy on three public datasets as well as our rendered multiple camera sRGB dataset. Our code is available at https: //github. com/ChunxiaoLe/WBFlow.

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IJCAI Conference 2022 Conference Paper

Domain Adversarial Learning for Color Constancy

Zhifeng Zhang
Xuejing Kang
Anlong Ming

Color Constancy aims to eliminate the color cast of RAW images caused by non-neutral illuminants. Though contemporary approaches based on convolutional neural networks significantly improve illuminant estimation, they suffer from the seriously insufficient data problem. To solve this problem by effectively utilizing multi-domain data, we propose the Domain Adversarial Learning Color Constancy (DALCC) which consists of the Domain Adversarial Learning Branch (DALB) and the Feature Reweighting Module (FRM). In DALB, the Camera Domain Classifier and the feature extractor compete against each other in an adversarial way to encourage the emergence of domain-invariant features. At the same time, the Illuminant Transformation Module performs color space conversion to solve the inconsistent color space problem caused by those domain-invariant features. They collaboratively avoid model degradation of multi-device training caused by the domain discrepancy of feature distribution, which enables our DALCC to benefit from multi-domain data. Besides, to better utilize multi-domain data, we propose the FRM that reweights the feature map to suppress Non-Primary Illuminant regions, which reduces the influence of misleading illuminant information. Experiments show that the proposed DALCC can more effectively take advantage of multi-domain data and thus achieve state-of-the-art performance on commonly used benchmark datasets.

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ICRA Conference 2022 Conference Paper

Fast Road Segmentation via Uncertainty-aware Symmetric Network

Yicong Chang
Feng Xue 0001
Fei Sheng
Wenteng Liang
Anlong Ming

The high performance of RGB-D based road segmentation methods contrasts with their rare application in commercial autonomous driving, which is owing to two reasons: 1) the prior methods cannot achieve high inference speed and high accuracy in both ways; 2) the different properties of RGB and depth data are not well-exploited, limiting the reliability of predicted road. In this paper, based on the evidence theory, an uncertainty-aware symmetric network (USNet) is proposed to achieve a trade-off between speed and accuracy by fully fusing RGB and depth data. Firstly, cross-modal feature fusion operations, which are indispensable in the prior RGB-D based methods, are abandoned. We instead separately adopt two light-weight subnetworks to learn road representations from RGB and depth inputs. The light-weight structure guarantees the real-time inference of our method. Moreover, a multi-scale evidence collection (MEC) module is designed to collect evidence in multiple scales for each modality, which provides sufficient evidence for pixel class determination. Finally, in uncertainty-aware fusion (UAF) module, the uncertainty of each modality is perceived to guide the fusion of the two sub-networks. Experimental results demonstrate that our method achieves a state-of-the-art accuracy with real-time inference speed of $43+$ FPS. The source code is available at https://github.com/morancyc/USNet.

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ICRA Conference 2022 Conference Paper

Monocular Depth Distribution Alignment with Low Computation

Fei Sheng
Feng Xue 0001
Yicong Chang
Wenteng Liang
Anlong Ming

The performance of monocular depth estimation generally depends on the amount of parameters and computational cost. It leads to a large accuracy contrast between light-weight networks and heavy-weight networks, which limits their application in the real world. In this paper, we model the majority of accuracy contrast between them as the difference of depth distribution, which we call 'Distribution drift'. To this end, a distribution alignment network (DANet) is proposed. We firstly design a pyramid scene transformer (PST) module to capture inter-region interaction in multiple scales. By perceiving the difference of depth features between every two regions, DANet tends to predict a reasonable scene structure, which fits the shape of distribution to ground truth. Then, we propose a local-global optimization (LGO) scheme to realize the supervision of global range of scene depth. Thanks to the alignment of depth distribution shape and scene depth range, DANet sharply alleviates the distribution drift, and achieves a comparable performance with prior heavy-weight methods, but uses only 1% floating-point operations per second (FLOPs) of them. The experiments on two datasets, namely the widely used NYUDv2 dataset and the more challenging iBims-1 dataset, demonstrate the effectiveness of our method. The source code is available at https://github.com/YiLiM1/DANet.

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IJCAI Conference 2022 Conference Paper

Rethinking Image Aesthetics Assessment: Models, Datasets and Benchmarks

Shuai He
Yongchang Zhang
Rui Xie
Dongxiang Jiang
Anlong Ming

Challenges in image aesthetics assessment (IAA) arise from that images of different themes correspond to different evaluation criteria, and learning aesthetics directly from images while ignoring the impact of theme variations on human visual perception inhibits the further development of IAA; however, existing IAA datasets and models overlook this problem. To address this issue, we show that a theme-oriented dataset and model design are effective for IAA. Specifically, 1) we elaborately build a novel dataset, called TAD66K, that contains 66K images covering 47 popular themes, and each image is densely annotated by more than 1200 people with dedicated theme evaluation criteria. 2) We develop a baseline model, TANet, which can effectively extract theme information and adaptively establish perception rules to evaluate images with different themes. 3) We develop a large-scale benchmark (the most comprehensive thus far) by comparing 17 methods with TANet on three representative datasets: AVA, FLICKR-AES and the proposed TAD66K, TANet achieves state-of-the-art performance on all three datasets. Our work offers the community an opportunity to explore more challenging directions; the code, dataset and supplementary material are available at https: //github. com/woshidandan/TANet.

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AAAI Conference 2022 Conference Paper

Transfer Learning for Color Constancy via Statistic Perspective

Yuxiang Tang
Xuejing Kang
Chunxiao Li
Zhaowen Lin
Anlong Ming

Color Constancy aims to correct image color casts caused by scene illumination. Recently, although the deep learning approaches have remarkably improved on single-camera data, these models still suffer from the seriously insufficient data problem, resulting in shallow model capacity and degradation in multi-camera settings. In this paper, to alleviate this problem, we present a Transfer Learning Color Constancy (TLCC) method that leverages cross-camera RAW data and massive unlabeled sRGB data to support training. Specifically, TLCC consists of the Statistic Estimation Scheme (SE- Scheme) and Color-Guided Adaption Branch (CGA-Branch). SE-Scheme builds a statistic perspective to map the camerarelated illumination labels into camera-agnostic form and produce pseudo labels for sRGB data, which greatly expands data for joint training. CGA-Branch further promotes efficient transfer learning from sRGB to RAW data by extracting color information to regularize the backbone’s features adaptively. Experimental results show the TLCC has overcome the data limitation and model degradation, outperforming the state-of-the-art performance on popular benchmarks. Moreover, the experiments also prove the TLCC is capable of learning new scenes information from sRGB data to improve accuracy on the RAW images with similar scenes.

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ICRA Conference 2019 Conference Paper

A Novel Multi-layer Framework for Tiny Obstacle Discovery

Feng Xue 0001
Anlong Ming
Menghan Zhou
Yu Zhou 0016

For tiny obstacle discovery in a monocular image, edge is a fundamental visual element. Nevertheless, because of various reasons, e. g. , noise and similar color distribution with background, it is still difficult to detect the edges of tiny (b) obstacles at long distance. In this paper, we propose an obstacle-aware discovery method to recover the missing contours of these obstacles, which helps to obtain obstacle proposals as much as possible. First, by using visual cues in monocular images, several multi-layer regions are elaborately inferred to reveal the distances from the camera. Second, several novel obstacle-aware occlusion edge maps are constructed to well capture the contours of tiny obstacles, which combines cues from each layer. Third, to ensure the existence of the tiny obstacle proposals, the maps from all layers are used for proposals extraction. Finally, based on these proposals containing tiny obstacles, a novel obstacle-aware regressor is proposed to generate an obstacle occupied probability map with high confidence. The convincing experimental results with comparisons on the Lost and Found dataset demonstrate the effectiveness of our approach, achieving around 9. 5% improvement on the accuracy than FPHT and PHT, it even gets comparable performance to MergeNet. Moreover, our method outperforms the state-of-the-art algorithms and significantly improves the discovery ability for tiny obstacles at long distance.

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IS Journal 2016 Journal Article

Monocular Depth-Ordering Reasoning with Occlusion Edge Detection and Couple Layers Inference

Anlong Ming
Tianfu Wu
Jianxiang Ma
Fang Sun
Yu Zhou

A depth-ordering reasoning approach first provides novel occlusion edge detection, generating precise same-layer relationship judgment and producing reliable region proposals for the depth-ordering inference. Specifically, a novel sparsity-induced regression model learns a discriminative feature subspace. In addition, kernel ridge regression assigns the occlusion label for each edge. The kernel trick guarantees linearly separable edges in a rich, high-dimensional feature space. Secondly, a couple layers inference approach infers the final depth order. In the semilocal layer, a novel triple descriptor judges the foreground relationship. In the global layer, the inference is executed by finding a valid path on a directed graph model. The proposed approach is validated on the Cornell depth-order and NYU 2 datasets.

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