arXiv subjects

Shaocong Wu

Publications and source records attributed to Shaocong Wu.

6 recordsLinked to original sources

arXiv2026

Preserving Forgery Artifacts: AI-Generated Video Detection at Native Scale

The rapid advancement of video generation models has enabled the creation of highly realistic synthetic media, raising significant societal concerns regarding the spread of misinformation. However, current detection methods suffer from critical limitations. They rely on preprocessing operations like fixed-resolution resizing and cropping. These operations not only discard subtle, high-frequency forgery traces but also cause spatial distortion and significant information loss. Furthermore, existing methods are often trained and evaluated on outdated datasets that fail to capture the sophistication of modern generative models. To address these challenges, we introduce a comprehensive dataset and a novel detection framework. First, we curate a large-scale dataset of over 140K videos from 15 state-of-the-art open-source and commercial generators, along with Magic Videos benchmark designed specifically for evaluating ultra-realistic synthetic content. In addition, we propose a novel detection framework built on the Qwen2.5-VL Vision Transformer, which operates natively at variable spatial resolutions and temporal durations. This native-scale approach effectively preserves the high-frequency artifacts and spatiotemporal inconsistencies typically lost during conventional preprocessing. Extensive experiments demonstrate that our method achieves superior performance across multiple benchmarks, underscoring the critical importance of native-scale processing and establishing a robust new baseline for AI-generated video detection.

cs.CV↗

arXiv2026

Consistency Beyond Contrast: Enhancing Open-Vocabulary Object Detection Robustness via Contextual Consistency Learning

Recent advances in open-vocabulary object detection focus primarily on two aspects: scaling up datasets and leveraging contrastive learning to align language and vision modalities. However, these approaches often neglect internal consistency within a single modality, particularly when background or environmental changes occur. This lack of consistency leads to a performance drop because the model struggles to detect the same object in different scenes, which reveals a robustness gap. To address this issue, we introduce Contextual Consistency Learning (CCL), a novel framework that integrates two key strategies: Contextual Bootstrapped Data Generation (CBDG) and Contextual Consistency Loss (CCLoss). CBDG functions as a data generation mechanism, producing images that contain the same objects across diverse backgrounds. This is essential because existing datasets alone do not support our CCL framework. The CCLoss further enforces the invariance of object features despite environmental changes, thereby improving the model's robustness in different scenes. These strategies collectively form a unified framework for ensuring contextual consistency within the same modality. Our method achieves state-of-the-art performance, surpassing previous approaches by +16.3 AP on OmniLabel and +14.9 AP on D3. These results demonstrate the importance of enforcing intra-modal consistency, significantly enhancing model generalization in diverse environments. Our code is publicly available at: https://github.com/bozhao-li/CCL.

cs.CV↗

arXiv2025

Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation

Dataset distillation compresses large-scale datasets into compact, highly informative synthetic data, significantly reducing storage and training costs. However, existing research primarily focuses on balanced datasets and struggles to perform under real-world long-tailed distributions. In this work, we emphasize the critical role of soft labels in long-tailed dataset distillation and uncover the underlying mechanisms contributing to performance degradation. Specifically, we derive an imbalance-aware generalization bound for model trained on distilled dataset. We then identify two primary sources of soft-label bias, which originate from the distillation model and the distilled images, through systematic perturbation of the data imbalance levels. To address this, we propose ADSA, an Adaptive Soft-label Alignment module that calibrates the entangled biases. This lightweight module integrates seamlessly into existing distillation pipelines and consistently improves performance. On ImageNet-1k-LT with EDC and IPC=50, ADSA improves tail-class accuracy by up to 11.8% and raises overall accuracy to 41.4%. Extensive experiments demonstrate that ADSA provides a robust and generalizable solution under limited label budgets and across a range of distillation techniques. Code is available at: https://github.com/j-cyoung/ADSA_DD.git.

cs.CV↗

arXiv2025

Parameterizing Dataset Distillation via Gaussian Splatting

Dataset distillation aims to compress training data while preserving training-aware knowledge, alleviating the reliance on large-scale datasets in modern model training. Dataset parameterization provides a more efficient storage structure for dataset distillation, reducing redundancy and accommodating richer information. However, existing methods either rely on complex auxiliary modules or fail to balance representational capacity and efficiency. In this paper, we propose GSDD, a simple, novel, and effective dataset parameterization technique for Dataset Distillation based on Gaussian Splatting. We adapt CUDA-based splatting operators for parallel training in batch, enabling high-quality rendering with minimal computational and memory overhead. Gaussian primitives can effectively capture meaningful training features, allowing a sparse yet expressive representation of individual images. Leveraging both high representational capacity and efficiency, GSDD substantially increases the diversity of distilled datasets under a given storage budget, thereby improving distillation performance. Beyond achieving competitive results on multiple standard benchmarks, GSDD also delivers significant performance gains on large-scale datasets such as ImageNet-1K and on video distillation tasks. In addition, we conduct comprehensive benchmarks to evaluate the computational efficiency, memory footprint, and cross-GPU architectural stability of GSDD. Code is available on https://github.com/j-cyoung/GSDatasetDistillation

cs.CV↗

arXiv2024

Collaborative planning of integrated hydrogen energy chain multi-energy systems: A review

Most planning of the traditional hydrogen energy supply chain (HSC) focuses on the storage and transportation links between production and consumption ends. It ignores the energy flows and interactions between each link, making it unsuitable for energy system planning analysis. Therefore, we propose the concept of a hydrogen energy chain (HEC) based on the HSC, which emphasizes the interactions between different types of energy flows in the production, compression, storage, transportation, and application links of hydrogen. The HEC plays a crucial role in mitigating fluctuations of renewable energy and facilitating the optimal allocation of heterogeneous energy sources across time and space. Effective collaborative planning models that consider HEC are essential for the optimal configuration of multi-energy systems (MESs), which guarantees high-efficiency operation and the economic and environmental friendliness of the system. This paper presents a systematic review of recent articles on collaborative planning of integrated hydrogen energy chain multi-energy systems (HEC-MESs). First, we introduce the basic framework of HEC-MES, focusing on the current research status of the production, compression, storage, transportation, and application links in HEC. Furthermore, we review technology types of hydrogen energy for planning and summarize the typical forms of HEC in MESs. Then, the following sections outline the models and methods for collaborative planning of HEC-MES. They include detailed analyses of covered sector types, spatial and temporal scopes of planning, uncertainties, model formulations, and solution methods. Finally, the paper concludes by summarizing the research gaps identified in current articles and outlining directions for future research.

eess.SY↗

arXiv2022

Unify Local and Global Information for Top-$N$ Recommendation

Knowledge graph (KG), integrating complex information and containing rich semantics, is widely considered as side information to enhance the recommendation systems. However, most of the existing KG-based methods concentrate on encoding the structural information in the graph, without utilizing the collaborative signals in user-item interaction data, which are important for understanding user preferences. Therefore, the representations learned by these models are insufficient for representing semantic information of users and items in the recommendation environment. The combination of both kinds of data provides a good chance to solve this problem. To tackle this research gap, we propose a novel duet representation learning framework named \sysname to fuse local information (user-item interaction data) and global information (external knowledge graph) for the top-$N$ recommendation, which is composed of two separate sub-models. One learns the local representations by discovering the inner correlations in local information with a knowledge-aware co-attention mechanism, and another learns the global representations by encoding the knowledge associations in global information with a relation-aware attention network. The two sub-models are jointly trained as part of the semantic fusion network to compute the user preferences, which discriminates the contribution of the two sub-models under the special context. We conduct experiments on two real-world datasets, and the evaluations show that KADM significantly outperforms state-of-art methods. Further ablation studies confirm that the duet architecture performs significantly better than either sub-model on the recommendation tasks.

cs.IR↗