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.

Jiayi Tuo, Cheng Tang, Zihan Wang, Chenyue Zhou, Yao Li, Yanbiao Ma, Chao Wang, Wei Dai, Mingxuan Wang, Shitong Qin, Ziwei Zhao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Despite the fast progress of deep learning, one standing challenge is the gap of the observed training samples and the underlying true distribution. There are multiple reasons for the causing of this gap e.g. sampling bias, noise etc. In the era of foundation models, we show that when leveraging the off-the-shelf (vision) foundation models (e.g., CLIP, DINOv2) for feature extraction, the geometric shapes of the resulting feature distributions exhibit remarkable transferability across domains and datasets. To verify its practical usefulness, we embody our geometric knowledge-guided distribution calibration framework in two popular and challenging settings: federated learning and long-tailed recognition. In the federated setting, we devise a technique of acquiring the global geometric shape under privacy constraints, then leverage this knowledge to generate new samples for clients, in the aim of bridging the gap between local and global observations. In long-tailed learning, it utilizes the geometric knowledge transferred from sample-rich categories to recover the true distribution for sample-scarce tail classes. Comprehensive experiments show that our proposed geometric knowledge-guided distribution calibration effectively overcomes information deficits caused by data heterogeneity and sample imbalance, with boosted performance across benchmarks.

Satellite video tracking presents significant challenges due to unpredictable target variations, environmental disturbances, and occlusions. Existing approaches either rely on auxiliary modalities or require full fine-tuning of foundation models, resulting in excessive parameter sensitivity and poor generalization. Meanwhile, conventional prompt-based tuning only updates parameters at a single location, limiting its ability to adapt to complex appearance changes. To address these limitations, we propose Adaptive Visual Prompting for Effective Satellite Video Tracking (AVPTrack). Unlike conventional prompts, introduced Super Prompts dynamically refine the original template at multiple distinct positions. This multi-location adaptation allows for fine-grained representation learning, enabling the tracker to better capture target variations and resist environmental disturbances. Additionally, Dynamic Templates are introduced to mitigate tracking failures in highly challenging scenarios, such as occlusions and background clutter, ensuring robust target localization. Furthermore, the Template Selection Adapter (TSA) selects the most relevant templates in real-time, enhancing tracking efficiency. These components are optimized during training while keeping other parameters frozen, ensuring parameter efficiency. We also investigate the relationship between fine-tuning proportions and learning rates to optimize model performance. Extensive evaluations on the SV248S, SatSOT, and VISO datasets demonstrate the superior adaptability and robustness of AVPTrack compared to existing methods.

Data heterogeneity in federated learning, characterized by a significant misalignment between local and global distributions, leads to divergent local optimization directions and hinders global model training. Existing studies mainly focus on optimizing local updates or global aggregation, but these indirect approaches demonstrate instability when handling highly heterogeneous data distributions, especially in scenarios where label skew and domain skew coexist. To address this, we propose a geometry-guided data generation method that centers on simulating the global embedding distribution locally. We first introduce the concept of the geometric shape of an embedding distribution and then address the challenge of obtaining global geometric shapes under privacy constraints. Subsequently, we propose GGEUR, which leverages global geometric shapes to guide the generation of new samples, enabling a closer approximation to the ideal global distribution. In single-domain scenarios, we augment samples based on global geometric shapes to enhance model generalization; in multi-domain scenarios, we further employ class prototypes to simulate the global distribution across domains. Extensive experimental results demonstrate that our method significantly enhances the performance of existing approaches in handling highly heterogeneous data, including scenarios with label skew, domain skew, and their coexistence. Code published at: https://github.com/WeiDai-David/2025CVPR_GGEUR

In object detection, the number of instances is commonly used to determine whether a dataset follows a long-tailed distribution, implicitly assuming that the model will perform poorly on categories with fewer instances. This assumption has led to extensive research on category bias in datasets with imbalanced instance distributions. However, even in datasets with relatively balanced instance counts, models still exhibit bias toward certain categories, indicating that instance count alone cannot explain this phenomenon. In this work, we first introduce the concept and measurement of category informativeness. We observe a significant negative correlation between a category’s informativeness and its accuracy, suggesting that informativeness more accurately reflects the learning difficulty of a category. Based on this observation, we propose the Informativeness-Guided Angular Margin Loss (IGAM Loss), which dynamically adjusts the decision space of categories according to their informativeness, thereby mitigating category bias in long-tailed datasets. IGAM Loss not only achieves superior performance on long-tailed benchmark datasets such as LVIS v1.0 and COCO-LT but also demonstrates significant improvements for underrepresented categories in non-long-tailed datasets like Pascal VOC. Extensive experiments confirm the potential of category informativeness as a tool and the generalizability of our proposed method.

Graph condensation (GC) has gained significant attention for its ability to synthesize smaller yet informative graphs. However, existing studies often overlook the robustness of GC in scenarios where the original graph is corrupted. In such cases, we observe that the performance of GC deteriorates significantly, while existing robust graph learning technologies offer only limited effectiveness. Through both empirical investigation and theoretical analysis, we reveal that GC is inherently an intrinsic-dimension-reducing process, synthesizing a condensed graph with lower classification complexity. Although this property is critical for effective GC performance, it remains highly vulnerable to adversarial perturbations. To tackle this vulnerability and improve GC robustness, we adopt the geometry perspective of graph data manifold and propose a novel **M**anifold-constrained **R**obust **G**raph **C**ondensation framework named **MRGC**. Specifically, we introduce three graph data manifold learning modules that guide the condensed graph to lie within a smooth, low-dimensional manifold with minimal class ambiguity, thereby preserving the classification complexity reduction capability of GC and ensuring robust performance under universal adversarial attacks. Extensive experiments demonstrate the robustness of MRGC across diverse attack scenarios.

Robust Federated Graph Learning (FGL) provides an effective decentralized framework for training Graph Neural Networks (GNNs) in noisy-label environments. However, the subtlety of noise during training presents formidable obstacles for developing robust FGL systems. Previous robust FL approaches neither adequately constrain edge-mediated error propagation nor account for intra-class topological differences. At the client level, we innovatively demonstrate that hyperspherical embedding can effectively capture graph structures in a fine-grained manner. Correspondingly, our method effectively addresses the aforementioned issues through fine-grained hypersphere alignment. Moreover, we uncover undetected noise arising from localized perspective constraints and propose the geometric-aware hyperspherical purification module at the server level. Combining both level strategies, we present our robust FGL framework,**HYPERION**, which operates all components within a unified hyperspherical space. **HYPERION** demonstrates remarkable robustness across multiple datasets, for instance, achieving a 29.7\% $\uparrow$ F1-macro score with 50\%-pair noise on Cora. The code is available for anonymous access at \url{https://anonymous.4open.science/r/Hyperion-NeurIPS/}.

This paper reviews the VCIP 2025 Grand Challenge on Live Broadcasting Video Quality Assessment. The competition aims to foster innovation in both subjective and objective VQA techniques tailored to live broadcasting videos, addressing the unique challenges posed by live streaming impairments while emphasizing the evaluation of QoE. The grand challenge used live broadcasting database LBVD which consists of 1013 videos focusing on distortion in live broadcasting videos. The competition had 14 participants and 5 teams submitted valid solutions for the final testing phase. The proposed solutions have shown significant progress in areas such as combining traditional feature engineering with deep learning models, achieved state-of-the-art performances for LBVD. Team ATHENA-Live-QoE and Team HZX Force tied for the first position. The dataset can be found at https://github.com/cpf0079/LBVD.

Building fair deep neural networks (DNNs) is a crucial step towards achieving trustworthy artificial intelligence. Delving into deeper factors that affect the fairness of DNNs is paramount and serves as the foundation for mitigating model biases. However, current methods are limited in accurately predicting DNN biases, relying solely on the number of training samples and lacking more precise measurement tools. Here, we establish a geometric perspective for analyzing the fairness of DNNs, comprehensively exploring how DNNs internally shape the intrinsic geometric characteristics of datasets-the intrinsic dimensions (IDs) of perceptual manifolds, and the impact of IDs on the fairness of DNNs. Based on multiple findings, we propose Intrinsic Dimension Regularization (IDR), which enhances the fairness and performance of models by promoting the learning of concise and ID-balanced class perceptual manifolds. In various image recognition benchmark tests, IDR significantly mitigates model bias while improving its performance.

To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes. However, recent studies have shown that tail classes are not always hard to learn, and model bias has been observed on sample-balanced datasets, suggesting the existence of other factors that affect model bias. In this work, we first establish a geometric perspective for analyzing model fairness and then systematically propose a series of geometric measurements for perceptual manifolds in deep neural networks. Subsequently, we comprehensively explore the effect of the geometric characteristics of perceptual manifolds on classification difficulty and how learning shapes the geometric characteristics of perceptual manifolds. An unanticipated finding is that the correlation between the class accuracy and the separation degree of perceptual manifolds gradually decreases during training, while the negative correlation with the curvature gradually increases, implying that curvature imbalance leads to model bias. We thoroughly validate this finding across multiple networks and datasets, providing a solid experimental foundation for future research. We also investigate the convergence consistency between the loss function and curvature imbalance, demonstrating the lack of curvature constraints in existing optimization objectives. Building upon these observations, we propose curvature regularization to facilitate the model to learn curvature-balanced and flatter perceptual manifolds. Evaluations on multiple long-tailed and non-long-tailed datasets show the excellent performance and exciting generality of our approach, especially in achieving significant performance improvements based on current state-of-the-art techniques. Our work opens up a geometric analysis perspective on model bias and reminds researchers to pay attention to model bias on non-long-tailed and even sample-balanced datasets.

Real-world data typically follows a long-tailed distribution. When a small sample of tail classes does not cover the underlying distribution well, methods such as class re-balancing strategies and decoupled training are difficult to work, and additional knowledge needs to be introduced to recover the underlying distribution of the tail classes. In this work, we observe that the similarity between the variances of the feature distributions increases with the class similarity. Then, we also find that well-represented feature distributions typically contain multiple subcenters, which allows for denser samples at the edges of the distribution and promotes model learning to more robust decision bounds. Based on these observations, we propose to calibrate the feature distribution of the tail class by transferring the variance of the feature distribution of the head class, and then sample from the calibrated tail class distribution to generate augmented samples. To coordinate with the tail class calibration method, we also propose label-aware noise suppression (LANS) for reducing the generation of noisy samples and a three-stage training scheme for reshaping decision boundaries and compacting feature learning. Experimental results on iNaturalist2018, ImageNet-LT, CIFAR-10-LT, and CIFAR-100-LT show that our method achieves state-of-the-art performance in most metrics compared to similar approaches.

To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes. However, recent studies have shown that tail classes are not always hard to learn, and model bias has been observed on sample-balanced datasets, suggesting the existence of other factors that affect model bias. In this work, we systematically propose a series of geometric measurements for perceptual manifolds in deep neural networks, and then explore the effect of the geometric characteristics of perceptual manifolds on classification difficulty and how learning shapes the geometric characteristics of perceptual manifolds. An unanticipated finding is that the correlation between the class accuracy and the separation degree of perceptual manifolds gradually decreases during training, while the negative correlation with the curvature gradually increases, implying that curvature imbalance leads to model bias. Therefore, we propose curvature regularization to facilitate the model to learn curvature-balanced and flatter perceptual manifolds. Evaluations on multiple long-tailed and non-long-tailed datasets show the excellent performance and exciting generality of our approach, especially in achieving significant performance improvements based on current state-of-the-art techniques. Our work opens up a geometric analysis perspective on model bias and reminds researchers to pay attention to model bias on non-long-tailed and even sample-balanced datasets. The code and model will be made public.

In scenarios with long-tailed distributions, the model's ability to identify tail classes is limited due to the under-representation of tail samples. Class rebalancing, information augmentation, and other techniques have been proposed to facilitate models to learn the potential distribution of tail classes. The disadvantage is that these methods generally pursue models with balanced class accuracy on the data manifold, while ignoring the ability of the model to resist interference. By constructing noisy data manifold, we found that the robustness of models trained on unbalanced data has a long-tail phenomenon. That is, even if the class accuracy is balanced on the data domain, it still has bias on the noisy data manifold. However, existing methods cannot effectively mitigate the above phenomenon, which makes the model vulnerable in long-tailed scenarios. In this work, we propose an Orthogonal Uncertainty Representation (hOUR) of feature embedding and an end-to-end training strategy to improve the long-tail phenomenon of model robustness. As a general enhancement tool, OUR has excellent compatibility with other methods and does not require additional data generation, ensuring fast and efficient training. Comprehensive evaluations on long-tailed datasets show that our method significantly improves the long-tail phenomenon of robustness, bringing consistent performance gains to other long-tailed learning methods.

The international charity SolarAid aims to provide access to lights in areas without electricity, and it is a challenge to accurately and efficiently transmit the lights to the areas in need. Multisource, multitemporal, and multimodal remote sensing images can provide rich information about the target area, so using multisource remote sensing images for accurate detection of human settlements without electricity is a feasible solution. In this paper two separate detection tasks are formulated: building two attention SENet for settlements detection and light detection using the Sentinel-2 dataset and the Suomi Visible Infrared Imaging Radiometer Suite (VIIRS) night time dataset, respectively. In addition, we study a new outlier removal method based on the pixel distribution characteristics of the VIIRS dataset for data pre-processing, and propose a post-processing method based on region continuity for further correction of the results. Experiments show that our method can maximize the use of multisource data information and rank first in the detection of settlements without electricity challenge track (Track DSE) of the 2021 IEEE GRSS Data Fusion Contest.