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KD-Crowd:a knowledge distillation framework for learning from crowds

Frontiers of Computer Science 2025年第1期19卷 119-130页

作者： Shaoyuan LI Yuxiang ZHENG Ye SHI Shengjun HUANG Songcan CHEN College of Computer Science and Technology Nanjing University of Aeronautics and AstronauticsMIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing 211106China

Recently, crowdsourcing has established itself as an efficient labeling solution by distributing tasks to crowd workers. As the workers can make mistakes with diverse expertise, one core learning task is to estimate each worker’s expertise, and aggregate over them to infer the latent true labels. In this paper, we show that as one of the major research directions, the noise transition matrix based worker expertise modeling methods commonly overfit the annotation noise, either due to the oversimplified noise assumption or inaccurate estimation. To solve this problem, we propose a knowledge distillation framework (KD-Crowd) by combining the complementary strength of noise-model-free robust learning techniques and transition matrix based worker expertise modeling. The framework consists of two stages: in Stage 1, a noise-model-free robust student model is trained by treating the prediction of a transition matrix based crowdsourcing teacher model as noisy labels, aiming at correcting the teacher’s mistakes and obtaining better true label predictions;in Stage 2, we switch their roles, retraining a better crowdsourcing model using the crowds’ annotations supervised by the refined true label predictions given by Stage 1. Additionally, we propose one f-mutual information gain (MIG^(f)) based knowledge distillation loss, which finds the maximum information intersection between the student’s and teacher’s prediction. We show in experiments that MIG^(f) achieves obvious improvements compared to the regular KL divergence knowledge distillation loss, which tends to force the student to memorize all information of the teacher’s prediction, including its errors. We conduct extensive experiments showing that, as a universal framework, KD-Crowd substantially improves previous crowdsourcing methods on true label prediction and worker expertise estimation.

关键词： crowdsourcing label noise worker expertise knowledge distillation robust learning

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Robust domain adaptation with noisy and shifted label distribution

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Frontiers of Computer Science 2025年第3期19卷 25-36页

作者： Shao-Yuan LI Shi-Ji ZHAO Zheng-Tao CAO Sheng-Jun HUANG Songcan CHEN MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjing 211106China

Unsupervised Domain Adaptation(UDA)intends to achieve excellent results by transferring knowledge from labeled source domains to unlabeled target domains in which the data or label distribution *** UDA methods have acquired great success when labels in the source domain are ***,even the acquisition of scare clean labels in the source domain needs plenty of costs as *** the presence of label noise in the source domain,the traditional UDA methods will be seriously degraded as they do not deal with the label *** this paper,we propose an approach named Robust Self-training with Label Refinement(RSLR)to address the above *** adopts the self-training framework by maintaining a Labeling Network(LNet)on the source domain,which is used to provide confident pseudo-labels to target samples,and a Target-specific Network(TNet)trained by using the pseudo-labeled *** combat the effect of label noise,LNet progressively distinguishes and refines the mislabeled source *** combination with class rebalancing to combat the label distribution shift issue,RSLR achieves effective performance on extensive benchmark datasets.

关键词： unsupervised domain adaptation label noise label distribution shift self-training class rebalancing

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Pushing one pair of labels apart each time in multi-label learning: from single positive to full labels

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Science China(Information Sciences) 2025年

作者： Xiang LI Xinrui WANG Songcan CHEN MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology/College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics

In multi-label learning (MLL), it is extremely challenging to accurately annotate every appearing object due to expensive costs and limited knowledge. When facing such a challenge, a more practical and cheaper alternative should be single positive multi-label learning (SPMLL), where only one positive label needs to be provided per sample. Existing SPMLL methods usually assume unknown labels as negatives, which inevitably introduces false negatives as noisy labels. More seriously, binary cross entropy (BCE) loss is often used for training, which is notoriously not robust to noisy labels. To mitigate this issue, we customize an objective function for SPMLL by pushing only one pair of labels apart each time to suppress the domination of negative labels, which is the main culprit of fitting noisy labels in SPMLL. To further combat such noisy labels, we explore the high-rankness of the label matrix, which can also push apart different labels. By directly extending from SPMLL to MLL with full labels, a unified loss applicable to both settings is derived. As a byproduct, the proposed loss can alleviate the imbalance inherent in MLL. Experiments on real datasets demonstrate that the proposed loss not only performs more robustly to noisy labels for SPMLL but also works well for full labels. Besides, we empirically discover that high-rankness can mitigate the dramatic performance drop in SPMLL. Most surprisingly, even without any regularization or fine-tuned label correction, only adopting our loss defeats state-of-the-art SPMLL methods on CUB, a dataset that severely lacks labels.

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Active Learning for Long-Tailed Annotation

Active Learning for Long-Tailed Annotation

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Lin Geng Ningzhong Liu Han Sun Jie Qin MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing University of Aeronautics and Astronautics Nanjing China Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Active learning (AL) is an effective method to balance annotation costs and model performance under resource-constrained circumstances. Most existing AL studies are typically designed for class-balanced datasets. However, the ubiquity of long-tailed distributions in real-world scenarios largely restricts the applicability of those AL methods. To tackle this problem, we propose a new active learning framework, namely long-tailed active learning (LTAL). The LTAL framework divides the long-tailed dataset into constantly evolving in-distribution (ID) and out-of-distribution (OOD) samples, and views the tail samples as OOD samples distinct from the head ones, thus intuitively converting the LTA problem into an iterative OOD detection task. We leverage an energy-based OOD detection approach with a well-designed class-imbalanced energy regularization loss to further extend the energy gap between head and tail classes, encouraging the model to select more unlabeled tail samples with higher free energy values. Experimental results show that despite its conceptual simplicity, the proposed method significantly outperforms competitive baselines.

关键词： Heavily-tailed distribution Uncertainty Costs Annotations Active learning Tail Signal processing Labeling Iterative methods Speech processing

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Aesthetics-guided Low-light Enhancement

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IEEE Transactions on pattern analysis and machine intelligence 2025年 PP卷 PP页

作者： Liang, Dong Gao, Yuanhang Li, Ling Xu, Zhengyan Huang, Sheng-Jun Chen, Songcan Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence College of Computer Science and Technology Nanjing China Nanjing University of Aeronautics and Astronautics Shenzhen Research Institute Shenzhen China

Evaluating the performance of low-light image enhancement (LLE) is highly subjective, thus making integrating human preferences into LLE a necessity. Existing methods fail to consider this and present a series of potentially valid heuristic criteria for training LLE models. In this paper, we propose a new paradigm, i.e., aesthetics-guided low-light image enhancement (ALL-E), which introduces aesthetic preferences to LLE and motivates training in a reinforcement learning framework with an aesthetic reward. Each pixel, functioning as an agent, refines itself by recursive actions. We further present ALL-E+, an extended version of ALL-E, which casts a two-stage aesthetics-guided enhancement and denoising. ALL-E+ achieves low-light enhancement and denoising compensation sequentially in a unified framework, resulting in significant improvements in both subjective visual experience and objective evaluation. Extensive experiments show that integrating aesthetic preferences can further improve the visual experience of enhanced images. Our results on various benchmarks also demonstrate the superiority of our method over state-of-the-art methods. © 1979-2012 IEEE.

关键词： Image denoising

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Filter, Obstruct and Dilute: Defending Against Backdoor Attacks on Semi-Supervised Learning

arXiv

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arXiv 2025年

作者： Wang, Xinrui Geng, Chuanxing Wan, Wenhai Li, Shao-Yuan Chen, Songcan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence China School of Computer Science and Technology Huazhong University of Science and Technology China

Recent studies have verified that semi-supervised learning (SSL) is vulnerable to data poisoning backdoor attacks. Even a tiny fraction of contaminated training data is sufficient for adversaries to manipulate up to 90% of the test outputs in existing SSL methods. Given the emerging threat of backdoor attacks designed for SSL, this work aims to protect SSL against such risks, marking it as one of the few known efforts in this area. Specifically, we begin by identifying that the spurious correlations between the backdoor triggers and the target class implanted by adversaries are the primary cause of manipulated model predictions during the test phase. To disrupt these correlations, we utilize three key techniques: Gaussian Filter, complementary learning and trigger mix-up, which collectively filter, obstruct and dilute the influence of backdoor attacks in both data pre-processing and feature learning. Experimental results demonstrate that our proposed method, Backdoor Invalidator (BI), significantly reduces the average attack success rate from 84.7% to 1.8% across different state-of-the-art backdoor attacks. It is also worth mentioning that BI does not sacrifice accuracy on clean data and is supported by a theoretical guarantee of its generalization capability. © 2025, CC BY.

关键词： Adversarial machine learning

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StructSR: Refuse Spurious Details in Real-World Image Super-Resolution

arXiv

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arXiv 2025年

作者： Li, Yachao Liang, Dong Ding, Tianyu Huang, Sheng-Jun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing211106 China Microsoft Washington United States

Diffusion-based models have shown great promise in real-world image super-resolution (Real-ISR), but often generate content with structural errors and spurious texture details due to the empirical priors and illusions of these models. To address this issue, we introduce StructSR, a simple, effective, and plug-and-play method that enhances structural fidelity and suppresses spurious details for diffusion-based Real-ISR. StructSR operates without the need for additional fine-tuning, external model priors, or high-level semantic knowledge. At its core is the Structure-Aware Screening (SAS) mechanism, which identifies the image with the highest structural similarity to the low-resolution (LR) input in the early inference stage, allowing us to leverage it as a historical structure knowledge to suppress the generation of spurious details. By intervening in the diffusion inference process, StructSR seamlessly integrates with existing diffusion-based Real-ISR models. Our experimental results demonstrate that StructSR significantly improves the fidelity of structure and texture, improving the PSNR and SSIM metrics by an average of 5.27% and 9.36% on a synthetic dataset (DIV2KVal) and 4.13% and 8.64% on two real-world datasets (RealSR and DRealSR) when integrated with four state-of-the-art diffusion-based Real-ISR methods. Code - https://***/LYCEXE/StructSR. Copyright © 2025, The Authors. All rights reserved.

关键词： Inference engines

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Generative Visual Commonsense Answering and Explaining with Generative Scene Graph Constructing

arXiv

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arXiv 2025年

作者： Yuan, Fan Fang, Xiaoyuan Quan, Rong Li, Jing Bi, Wei Xu, Xiaogang Li, Piji College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Ministry of Education Nanjing211106 China Department of Computing The Hong Kong Polytechnic University Hong Kong The Chinese University of Hong Kong Hong Kong

Visual Commonsense Reasoning, which is regarded as one challenging task to pursue advanced visual scene comprehension, has been used to diagnose the reasoning ability of AI systems. However, reliable reasoning requires a good grasp of the scene’s details. Existing work fails to effectively exploit the real-world object relationship information present within the scene, and instead overly relies on knowledge from training memory. Based on these observations, we propose a novel scene-graph-enhanced visual commonsense reasoning generation method named G2, which first utilizes the image patches and LLMs to construct a location-free scene graph, and then answer and explain based on the scene graph’s information. We also propose automatic scene graph filtering and selection strategies to absorb valuable scene graph information during training. Extensive experiments are conducted on the tasks and datasets of scene graph constructing and visual commonsense answering and explaining, respectively. Experimental results and ablation analysis demonstrate the effectiveness of our proposed framework. Copyright © 2025, The Authors. All rights reserved.

关键词： Question answering

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E²GO: Free Your Hands for Smartphone Interaction

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International Journal of Human-Computer Interaction 2025年

作者： Yan, Shaoming Ju, Yuanliang Quan, Rong Tu, Huawei Liang, Dong MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Collaborative Innovation Center of Novel Software Technology and Industrialization College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Science and Information Technology La Trobe University Melbourne Australia

Current eye-gaze interaction technologies for smartphones are considered inflexible, inaccurate, and power-hungry. These methods typically rely on hand involvement and accomplish partial interactions. In this paper, we propose a novel eye-gaze smartphone interaction method named Event-driven Eye-Gaze Operation ((Formula presented.)), which can realize comprehensive interaction using only eyes and gazes to cover various interaction types. Before the interaction, an anti-jitter gaze estimation method was exploited to stabilize human eye fixation and predict accurate and stable human gaze positions on smartphone screens to further explore refined time-dependent eye-gaze interactions. We also integrated an event-triggering mechanism in (Formula presented.) to significantly decrease its power consumption to deploy on smartphones. We have implemented the prototype of (Formula presented.) on different brands of smartphones and conducted a comprehensive user study to validate its efficacy, demonstrating (Formula presented.) ‘s superior smartphone control capabilities across various scenarios. Demo videos. © 2025 Taylor & Francis Group, LLC.

关键词： Smartphones

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Handling Noisy Annotation for Remote Sensing Semantic Segmentation via Boundary-Aware Knowledge Distillation

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Sun, Yue Liang, Dong Li, Shao-Yuan Chen, Songcan Huang, Sheng-Jun Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing211106 China Shenzhen Research Institute Nanjing University of Aeronautics and Astronautics Shenzhen518131 China Joint Laboratory of Spatial Intelligent Perception and Large Model Application Nanjing211106 China

In recent years, image segmentation has made significant progress, but acquiring annotated data is still a considerable challenge, especially in remote sensing imagery (RSI). The complex structure and intercategory confusion of RSI increase the time consumption and cost of pixel-level annotation, and noisy annotations inevitably appear. This article proposes a boundary-aware knowledge distillation (BAKD) method to handle noisy annotations by evaluating their uncertainty. BAKD consists of two core strategies: predictive confidence evaluation (PCE) and boundary-annotated reliability evaluation (BRE). The predictive confidence (PC) jointly decided by the teacher and student networks reflects the annotation’s uncertainty. The boundary-annotated reliability (BR) directly measures the annotation’s uncertainty based on the distance from the annotation to the semantic boundary. Leveraging these two types of uncertainty information, BAKD assigns each sample a comprehensive boundary-aware weight to identify samples with potential noisy annotations. This alleviates the impact of noisy annotation on the model’s training and improves its generalization performance. Experimental results show that BAKD achieves competitive semantic segmentation performance on the Potsdam and Vaihingen benchmarks compared with the state-of-the-art KD methods. In addition, BAKD can be easily integrated into semantic segmentation methods based on KD (SSKD), extending their applicability in handling noisy annotations. © 1980-2012 IEEE.

关键词： Semantic Segmentation

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