Person re-identification (ReID) is a fundamental task in many real-world applications such as pedestrian trajectory tracking. However, advanced deep learning-based ReID models are highly susceptible to adversarial attacks, where imperceptible perturbations to pedestrian images can cause entirely incorrect predictions, posing significant security threats. Although numerous adversarial defense strategies have been proposed for classification tasks, their extension to metric learning tasks such as person ReID remains relatively unexplored. Moreover, the several existing defenses for person ReID fail to address the inherent unique challenges of adversarially robust ReID. In this paper, we systematically identify the challenges of adversarial defense in person ReID into two key issues: model bias and composite generalization requirements. To address them, we propose a debiased dual-invariant defense framework composed of two main phases. In the data balancing phase, we mitigate model bias using a diffusion-model-based data resampling strategy that promotes fairness and diversity in training data. In the bi-adversarial self-meta defense phase, we introduce a novel metric adversarial training approach incorporating farthest negative extension softening to overcome the robustness degradation caused by the absence of classifier. Additionally, we introduce an adversarially-enhanced self-meta mechanism to achieve dual-generalization for both unseen identities and unseen attack types. Experiments demonstrate that our method significantly outperforms existing state-of-the-art defenses.
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How far are deep models from real-world video anomaly understanding (VAU)? Current works typically emphasize detecting unexpected occurrences deviating from normal patterns or comprehending anomalous events with interpretable descriptions. However, they exhibit only a superficial comprehension of real-world anomalies, with limited breadth in complex principles and subtle contexts that distinguish the anomalies from normalities, e.g., climbing cliffs with safety gear vs. without it. To this end, we introduce CueBench, the first of its kind Benchmark, devoted to Context-aware video anomalies within a Unified Evaluation framework. We comprehensively establish an event-centric hierarchical taxonomy that anchors two core event types: 14 conditional and 18 absolute anomaly events, defined by their refined semantics from diverse contexts across 174 scenes and 198 attributes. Based on this, we propose to unify and benchmark context-aware VAU with various challenging tasks across recognition, temporal grounding, detection, and anticipation. It also serves as a rigorous and fair probing evaluation suite for generalized and specialized vision-language models (VLMs) across both generative and discriminative paradigms. To address the challenges underlying CueBench, we further develop Cue-R1 based on R1-style reinforcement fine-tuning with verifiable, task-aligned, and hierarchy-refined rewards in a unified generative manner. Extensive results on CueBench reveal that, existing VLMs are still far from satisfactory real-world anomaly understanding, while our Cue-R1 surpasses these state-of-the-art approaches by over 24% on average.
Fair incomplete multi-view clustering (FIMVC) confronts a critical yet unresolved challenge, as existing methods often fail to address the intertwined issues of data missingness and algorithmic bias simultaneously. In this paper, we propose a novel FIMVC method named Adversarial Fair Incomplete Multi-View Clustering (AFIMVC). The core of AFIMVC is a new adaptive adversarial disentanglement mechanism. This mechanism trains the feature encoder to produce representations that are invariant to sensitive attributes by adversary learning, where the adversarial intensity is dynamically controlled by the model's real-time bias. Additionally, we develop a probabilistic cross-view contrastive learning strategy to achieve semantic consistency in latent space. To handle missing data, AFIMVC employs a context-aware fusion strategy that leverages cross-sample attention to robustly synthesize a unified representation from incomplete views. Extensive experiments demonstrate that AFIMVC achieves a state-of-the-art balance between clustering accuracy and fairness, significantly outperforming existing methods.
Visual attribute imbalance is a common yet underexplored issue in image classification, significantly impacting model performance and generalization. In this work, we first define the first-level and second-level attributes of images and then introduce a CLIP-based framework to construct a visual attribute dictionary, enabling automatic evaluation of image attributes. By systematically analyzing both single-attribute imbalance and compositional attribute imbalance, we reveal how the rarity of attributes affects model performance. To tackle these challenges, we propose adjusting the sampling probability of samples based on the rarity of their compositional attributes. This strategy is further integrated with various data augmentation techniques (such as CutMix, Fmix, and SaliencyMix) to enhance the model's ability to represent rare attributes. Extensive experiments on benchmark datasets demonstrate that our method effectively mitigates attribute imbalance, thereby improving the robustness and fairness of deep neural networks. Our research highlights the importance of modeling visual attribute distributions and provides a scalable solution for long-tail image classification tasks.
While deep generative models have significantly advanced representation learning, they may inherit or amplify biases and fairness issues by encoding sensitive attributes alongside predictive features. Enforcing strict independence in disentanglement is often unrealistic when target and sensitive factors are naturally correlated. To address this challenge, we propose CAD-VAE(Correlation-Aware Disentangled VAE), which introduces a correlated latent code to capture the information shared between the target and sensitive attributes. Given this correlated latent, our method effectively separates overlapping factors without extra domain knowledge by directly minimizing the conditional mutual information between target and sensitive codes. A relevance-driven optimization strategy refines the correlated code by efficiently capturing essential correlated features and eliminating redundancy. Extensive experiments on benchmark datasets demonstrate that CAD-VAE produces fairer representations, realistic counterfactuals, and improved fairness-aware image editing.
Purple fringing, a persistent artifact caused by Longitudinal Chromatic Aberration (LCA) in camera lenses, has long degraded the clarity and realism of digital imaging. Traditional solutions rely on complex and expensive apochromatic (APO) lens hardware and the extraction of handcrafted features, ignoring the data-driven approach. To fill this gap, we introduce DCA-LUT, the first deep learning framework for purple fringing removal. Inspired by the physical root of the problem-the spatial misalignment of RGB color channels due to lens dispersion, we introduce a novel Chromatic-Aware Coordinate Transformation (CA-CT) module, learning an image-adaptive color space to decouple and isolate fringing into a dedicated dimension. This targeted separation allows the network to learn a precise "purple fringe channel," which then guides the accurate restoration of the luminance channel. The final color correction is performed by a learned 5D Look-Up Table (5D LUT), enabling efficient and powerful non-linear color mapping. To enable robust training and fair evaluation, we constructed a large-scale synthetic purple fringing dataset (PF-Synth). Extensive experiments in synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance in purple fringing removal.
Fair Facial Attribute Recognition via Group-Decoupled Vision Transformer with Mask-Guided Correlation Suppression
PDF ↗Facial Attribute Recognition (FAR) holds significant potential for wide-ranging applications. However, traditionally trained FAR models exhibit unfairness, largely due to data bias—where certain sensitive attributes correlate statistically with target attributes. To address this, we propose a group-attention mechanism: first, each image is categorized into subgroups (e.g., Male/Female&short hair, Male/Female&long hair). Within the attention mechanism, distinct Query parameters are used for each group, with shared Key and Value parameters. As group-specific Query parameters are trained on subgrouped data, the noted bias is effectively mitigated. Consequently, integrating this Group-Attention into Vision Transformer (ViT) yields our novel Group-Decoupled ViT (GD-ViT) model. Moreover, to further attenuate the statistical correlation between sensitive and target attributes, we propose a Mask-Guided Correlation Suppression learning strategy. Specifically, in Stage 1, it first leverages a min-max dual-loss optimization strategy to train GD-ViT in capturing key regions related to sensitive attributes yet irrelevant to target attributes. Then, in Stage 2, it trains another GD-ViT by masking sensitive regions identified in Stage 1, fusing the masked output (as intermediate input) with the model’s intermediate outputs. This weakens regions associated with sensitive attributes while enhancing others, suppressing the learning of key features related to sensitive attributes. Consequently, it encourages the model to focus more on intrinsic target attribute regions and balances the learning process between the sensitive attribute and the target attribute. Extensive experiments demonstrate that our method achieves superior performance across three benchmark datasets for fair facial attribute recognition.
Deep neural networks have demonstrated remarkable performance across various domains, yet their decision-making processes remain opaque. Although many explanation methods are dedicated to bringing the obscurity of DNNs to light, they exhibit significant limitations: post-hoc explanation methods often struggle to faithfully reflect model behaviors, while self-explaining neural networks sacrifice performance and compatibility due to their specialized architectural designs. To address these challenges, we propose a novel self-explaining framework that integrates Shapley value estimation as an auxiliary task during training, which achieves two key advancements: 1) a fair allocation of the model prediction scores to image patches, ensuring explanations inherently align with the model's decision logic, and 2) enhanced interpretability with minor structural modifications, preserving model performance and compatibility. Extensive experiments on multiple benchmarks demonstrate that our method achieves state-of-the-art interpretability.
Rethinking Long-tailed Dataset Distillation: A Uni-Level Framework with Unbiased Recovery and Relabeling
PDF ↗Dataset distillation creates a small distilled set that enables efficient training by capturing key information from the full dataset. While existing dataset distillation methods perform well on balanced datasets, they struggle under long-tailed distributions, where imbalanced class frequencies induce biased model representations and corrupt statistical estimates such as Batch Normalization (BN) statistics. In this paper, we rethink long-tailed dataset distillation by revisiting the limitations of trajectory-based methods, and instead adopt the statistical alignment perspective to jointly mitigate model bias and restore fair supervision. To this end, we introduce three dedicated components that enable unbiased recovery of distilled images and soft relabeling: (1) enhancing expert models (an observer model for recovery and a teacher model for relabeling) to enable reliable statistics estimation and soft-label generation; (2) recalibrating BN statistics via a full forward pass with dynamically adjusted momentum to reduce representation skew; (3) initializing synthetic images by incrementally selecting high-confidence and diverse augmentations via a multi-round mechanism that promotes coverage and diversity. Extensive experiments on four long-tailed benchmarks show consistent improvements over state-of-the-art methods across varying degrees of class imbalance.Notably, our approach improves top-1 accuracy by 15.6% on CIFAR-100-LT and 11.8% on Tiny-ImageNet-LT under IPC=10 and IF=10.
The potential of Generalized Category Discovery (GCD) lies in its ability to identify previously undiscovered patterns in both labeled and unlabeled data by leveraging insights from partially labeled training samples. However, interference can arise due to the model's dual focus on discovering both novel and known categories, often leading to conflicts that obscure true patterns in the dataset. This paper presents a divide-and-conquer framework, Foundation-Adaptive Integrated Refinement (FAIR), which fine-tunes pretrained foundational weights for various purposes, divided into Foundation (pretrained weights), Adaptive (weights fine-tuned with a variance-preserving loss), and Integrated (weights adjusted for both labeled and unlabeled data). The Adaptive utilizes a newly proposed adaptive contrastive loss that introduces variances within classes to preserve the individuality of representations. The Integrated addresses inherent estimation errors while dynamically estimating the number of categories, incorporating a cosine-based perturbation mechanism as a relaxed margin to accommodate potential ground-truth deviations, rather than relying on biased estimates. Extensive experiments on six benchmark datasets demonstrate our method's effectiveness, outperforming state-of-the-art algorithms, especially on fine-grained datasets.
As large language models (LLMs) increasingly operate as autonomous agents in social contexts, evaluating their capacity for prosocial behavior is both theoretically and practically critical. However, existing research has primarily relied on static, economically framed paradigms, lacking models that capture the dynamic evolution of prosociality and its sensitivity to structural inequities. To address these gaps, we introduce ProSim, a simulation framework for modeling the prosocial behavior in LLM agents across diverse social conditions. We conduct three progressive studies to assess prosocial alignment. First, we demonstrate that LLM agents can exhibit human-like prosocial behavior across a broad range of real-world scenarios and adapt to normative policy interventions. Second, we find that agents engage in fairness-based third-party punishment and respond systematically to variations in inequity magnitude and enforcement cost. Third, we show that policy-induced inequities suppress prosocial behavior, propagate norm erosion through social networks. These findings advance prosocial behavior theory by elucidating how institutional dynamics shape the emergence, decay, and diffusion of prosocial norms in agent-driven societies.
Exploring Selective Avoidance for Online User Behavior Analysis: A Forest of Thought Explanation
PDF ↗The response behaviors observed in online user-generated content (UGC) frequently demonstrate non-linear characteristics, such as conditional branching and selective avoidance. These patterns present additional challenges for ensuring the trustworthiness of Large Language Model (LLMs) reasoning, particularly as their unidirectional, left-to-right inference mechanisms may not adequately capture such complex reasoning dynamics. To address this, we propose a Forest of Thought Explanation (FoTE), a novel prompting that models the selective avoidance in UGC while ensuring explanation consensus through reasoning paths across all decision sub-trees. FoTE firstly generates various reasoning paths through an adaptive CoT prompting. Each generated thought is subsequently evaluated through cooperative game theory to quantify its fair influence. The thoughts with the top-k contribution scores are preserved and randomly sampled to emulate selective avoidance for the next reasoning iteration. Through extensive evaluations across three open-source LLMs and two established social science problems (spanning four benchmark datasets), FoTE demonstrates superior success rates compared to competing prompting strategies. Notably, its performance gains increase with the strength of selective avoidance in social problems. The trustworthiness of our FoTE is enhanced by the incorporation of (1) a solid theoretical foundation and (2) a transparent reasoning path that converges toward consensus.
Video-based commonsense captioning aims to generate captions for the video content while providing multiple commonsense about the underlying events. Existing approaches rely on constructing a "video -> content caption -> commonsense" reasoning chain, which generates visually ungrounded commonsense and neglects inter-category commonsense correlations. Firstly, the existing reasoning chain induces the model's excessive reliance on content caption when generating commonsense, resulting in generic outputs with limited visual relevance. Secondly, the reasoning chain adopts multiple isolated decoders for commonsense generation, which fails to leverage the correlations between different categories of commonsense. To address these limitations, we introduce a novel self-critical distillation network (SCD-Net), which optimizes the reasoning chain by enhancing visual reasoning and establishing inter-category commonsense correlations. Specifically, on the one hand, we introduce self-critical learning and design a reward function to allow the model to refine its output. This mechanism incentivizes the model to maximize the utilization of visual information, thus improving the model's capacity for visual comprehension. On the other hand, we propose a joint reasoning distillation framework that fosters mutual inference among diverse commonsense categories. In this framework, we incorporate the cascaded decoder and knowledge distillation strategy to facilitate inter-category commonsense knowledge transfer while maintaining the fairness of the testing. Our experiments on the large-scale Video-to-Commonsense dataset demonstrate that our approach performs favorably against state-of-the-art methods. The code will be released soon.
Curriculum Group Policy Optimization: Adaptive Sampling for Unleashing the Potential of Text-to-Image Generation
PDF ↗Text-to-Image (T2I) generation has achieved remarkable progress in recent years. Meanwhile, reinforcement learning methods, particularly those based on Group Relative Policy Optimization (GRPO), have attracted widespread attention and been successfully applied to T2I tasks. However, the uniform sampling strategy commonly used during training often ignores the match between sample difficulty and the model's current learning capability, leading to low training efficiency. We argue that improving training efficiency requires continuously prioritizing prompts that match the model's evolving capability and remain actively learnable. To this end, we propose Curriculum Group Policy Optimization (CGPO), an adaptive curriculum training framework. During training, each prompt produces a group of images scored by a reward model. We use the variance of group rewards as an online proxy for prompt inconsistency. A higher variance suggests that the model has partially captured the prompt requirements but has not yet achieved stable mastery. Such prompts are more likely to provide useful learning signals, so we increase their sampling probabilities accordingly. Additionally, to address data imbalance in multi-category datasets, we design a category calibration method based on proportional fairness optimization, which balances training difficulty across categories. Experiments on GenEval, T2I-CompBench++, and DPG Bench demonstrate that our framework effectively improves generation performance.
Heuristic Self-Paced Learning for Domain Adaptive Semantic Segmentation under Adverse Conditions
PDF ↗The learning order of semantic classes significantly impacts unsupervised domain adaptation for semantic segmentation, especially under adverse weather conditions. Most existing curricula rely on handcrafted heuristics (e.g., fixed uncertainty metrics) and follow a static schedule, which fails to adapt to a model's evolving, high-dimensional training dynamics, leading to category bias. Inspired by Reinforcement Learning, we cast curriculum learning as a sequential decision problem and propose an autonomous class scheduler. This scheduler consists of two components: (i) a high-dimensional state encoder that maps the model's training status into a latent space and distills key features indicative of progress, and (ii) a category-fair policy-gradient objective that ensures balanced improvement across classes. Coupled with mixed source-target supervision, the learned class rankings direct the network's focus to the most informative classes at each stage, enabling more adaptive and dynamic learning. It is worth noting that our method achieves state-of-the-art performance on three widely used benchmarks (e.g., ACDC, Dark Zurich, and Nighttime Driving) and shows generalization ability in synthetic-to-real semantic segmentation.
Federated Graph Learning (FGL) offers a privacy-preserving paradigm for collaborative training on graph data, yet significant topological heterogeneity poses a critical threat to generalization fairness, often yielding a global model dominated by a subset of clients. This introduces two critical issues: at the global level, aggregation bias disproportionately amplifies the influence of dominant clients, while at the local level, blind optimization results in inefficient and inequitable training processes. To address these challenges, we propose FedSST, an adaptive fairness framework. FedSST introduces a fair, structure-based signal to quantify client contributions, which in turn guides fair aggregation and adaptive local training. Extensive experiments across diverse cross-domain and cross-dataset settings demonstrate that FedSST enhances generalization fairness and overall model performance, outperforming various state-of-the-art methods.
FairLLaVA: Fairness-Aware Parameter-Efficient Fine-Tuning for Large Vision-Language Assistants
PDF ↗While powerful in image-conditioned generation, multimodal large language models (MLLMs) can display uneven performance across demographic groups, highlighting fairness risks. In safety-critical clinical settings, such disparities risk producing unequal diagnostic narratives and eroding trust in AI-assisted decision-making. While fairness has been studied extensively in vision-only and language-only models, its impact on MLLMs remains largely underexplored. To address these biases, we introduce FairLLaVA, a parameter-efficient fine-tuning method that mitigates group disparities in visual instruction tuning without compromising overall performance. By minimizing the mutual information between target attributes, FairLLaVA regularizes the model's representations to be demographic-invariant. The method can be incorporated as a lightweight plug-in, maintaining efficiency with low-rank adapter fine-tuning, and provides an architecture-agnostic approach to fair visual instruction following. Extensive experiments on large-scale chest radiology report generation and dermoscopy visual question answering benchmarks show that FairLLaVA consistently reduces inter-group disparities while improving both equity-scaled clinical performance and natural language generation quality across diverse medical imaging modalities. Code can be accessed at https://github.com/bhosalems/FairLLaVA.
Bias Is a Subspace, Not a Coordinate: A Geometric Rethinking of Post-hoc Debiasing in Vision-Language Models
PDF ↗Vision-Language Models (VLMs) have become indispensable for multimodal reasoning, yet their representations often encode and amplify demographic biases, resulting in biased associations and misaligned predictions in downstream tasks. Such behavior undermines fairness and distorts the intended alignment between vision and language. Recent post-hoc approaches attempt to mitigate bias by replacing the most attribute-correlated embedding coordinates with neutral values. However, our systematic analysis reveals three critical limitations of this coordinate-wise approach: feature entanglement, poor cross-dataset generalization, and incomplete bias removal. We find that bias is not localized to a few coordinates but is instead distributed across a few linear subspaces. To address these limitations, we propose Subspace Projection Debiasing (SPD), a geometrically principled framework that identifies and removes the entire subspace of linearly decodable bias while reinserting a neutral mean component to preserve semantic fidelity. Extensive experiments across zero-shot classification, text-to-image retrieval, and image generation validate the effectiveness of SPD: our method achieves more robust debiasing with an average improvement of 18.5% across four fairness metrics, while maintaining minimal loss in task performance compared to the best debiasing baseline.
Image abstraction, a fundamental component of non-photorealistic rendering (NPR), aims to simplify photographs into stylized depictions while preserving perceptually important structures. A central difficulty is selectivity: removing fine textures while preserving semantically meaningful boundaries. Existing approaches often expose only a few entangled controls, so the amount of smoothing and the set of preserved structures cannot be adjusted in a clear and intuitive manner. We propose the Semantic Scale Space (SSS), a two-dimensional abstraction framework parameterized by smoothing strength and semantic granularity. SSS externalizes the stopping set by using a controllable semantic boundary detector to specify which structures act as barriers to smoothing, independently of how far simplification proceeds within homogeneous regions. We further introduce Adaptive Granularity Scheduling Smoothing (AGSS), a concrete traversal policy in this space that combines a donor-gated diffusion operator with a fine-to-coarse granularity schedule. To evaluate selectivity fairly, we also introduce an effect-matched protocol based on a Region Homogeneity Index, which compares methods under matched smoothing levels. On the SBD benchmark, AGSS achieves higher boundary preservation and lower geometric drift than strong baselines at the same degree of smoothing. On DIV2K, a user study further shows that downstream NPR results built on AGSS-based abstractions are consistently preferred. These results demonstrate that SSS and AGSS provide a practical framework and method for controllable image abstraction in modern creative applications.
Ensuring fairness in medical vision-language models (VLMs) is essential for equitable healthcare, yet existing models amplify biases across demographic subgroups such as race and gender. Traditional fairness mitigation approaches relying on broad distribution alignment, fall short in addressing these nuanced intersectional disparities. We propose fairness-aware relational prompting (FRP), a novel framework that reformulates prompt generation as a dynamic, fairness-aware reasoning process. FRP constructs a relational graph to capture fine-grained, sample-level similarities and employs a hyperbolic graph layer to explicitly model the hierarchical structure of intersectional identities. Leveraging hyperbolic geometry enables reasoning over complex attribute combinations, effectively reducing entrenched biases. Evaluations on the FairVLMed and Harvard-GF datasets demonstrate that FRP achieves state-of-the-art diagnostic performance, with an area under the curve of 77.50% and 85.94% respectively, while substantially improving the demographic parity difference and equalized odds difference.