Artificial Intelligence (AI) systems rapidly advance in online medical consultations, where doctors diagnose through online dialogue. Recent AI models have made significant progress in symptom inquiry; however, the disease diagnosis accuracy remains low and unreliable, failing to replace doctors’ role fully. Although some studies attempt to assist doctors by providing AI-generated advice, this advice often has high error rates and lacks complementarity, needing further improvement. Therefore, we aim to introduce a reliable and effective human-AI collaboration system. There are two key challenges. 1) How to design an advice strategy that improves the accuracy of the advice? 2) How to develop an optimal AI teammate for the human-AI team to enhance the overall team utility? To address these challenges, we propose the Human-AI collaboration diagnosis framework with Combination advice (HAComb). Specifically, to ensure the accuracy of advice, we introduce a human-AI combination advice that uses Bayesian methods to integrate doctors’ predicted labels with AI model outputs. To enhance team utility, we design a loss function that incorporates both AI loss and team utility loss. Experiments on four real-world datasets show that HAComb outperforms single human and other human-AI collaboration methods in diagnosis accuracy and team utility.
Visible-Infrared Person Re-Identification (VI-ReID) seeks to match individuals across different modalities. Recent methods focus on discriminative feature extraction and hard sample learning. However, they often suffer from semantic misalignment due to horizontal partitioning in local feature extraction and overlook global hard samples in training. Moreover, the widely used PK Sampler cannot ensure viewpoint balance and diversity. To overcome these limitations, we propose the Semantic Alignment and Hard Sample Retraining (SAHSR) framework. This framework incorporates a Recurrent Semantic Aggregation (RSA) module that progressively aggregates and aligns regional semantics with the help of Modality Alignment loss. Besides, we propose a Confidence-based Hard Sample Retraining (CHSR) strategy that identifies and retrains hard samples to improve the model’s robustness. Additionally, we introduce the Viewpoint-Balanced (VB) Sampler to guarantee a balanced distribution of viewpoints. Extensive experiments on VI-ReID benchmarks demonstrate the significant performance gains of our approach, showing state-of-the-art performance. Code will be available.
Real-time conferencing applications (RCA) are crucial for online learning and e-commerce. However, they can be affected by network fluctuations because they are heavily dependent on cloud network connections. However, there is a dearth of systematic studies that aim to pinpoint the specific network links where these fluctuations occur. We introduce a contrastive learning approach for locating anomalies, based on actual traffic from real-time conferencing applications. This method is trained on unlabeled data, which means that it does not require the creation of a large-scale training dataset. The results illustrate the robust localization ability, achieving an accuracy rate of more than 95%, demonstrating its adaptability to commonly used real-time conferencing applications.
Tabular data is one of the most common data formats, and recent advancements in deep learning have driven significant progress in tabular data synthesis. However, the complexity of mixed-type distributions in tabular data forces existing synthesis methods to rely on lossy preprocessing, leading to significant logical inconsistencies in synthetic data. Moreover, most approaches lack flexible conditional control over the synthesis process. In this paper, we introduce a novel two-stage tabular data synthesis method LogiCoTab, which fundamentally mitigates logical inconsistencies and enhances controllability in synthetic data. In the logical awareness stage, we design a dual-module architecture to extract semantic features and uncover intricate inter-column logical relationships. In the controlled synthesis stage, we develop a diffusion-based generative model enabling highly flexible and precise conditional data generation. Extensive experiments show that LogiCoTab outperforms state-of-the-art methods on multiple datasets, significantly enhancing tabular data synthesis quality.
Text-guided character animation generation has emerged as a significant research area with broad applications in gaming, film, interactive media, and beyond. However, existing motion-language datasets face limitations in motion quality, stylistic diversity, and annotation depth, particularly for professional applications. In contrast to existing datasets based on motion capture or video reconstruction techniques, our dataset leverages professionally crafted game animations and employs a structured annotation framework that incorporates standardized game design terminology. The dataset contains 8,700 high-fidelity motion sequences paired with 26,100 multi-level textual descriptions, generated through our proposed annotation pipeline that combines domain expertise with large language models. Through comprehensive experiments and user studies, we demonstrate GameMLD’s advantages in motion quality, style expressiveness, and annotation quality. Additionally, we showcase its practical value by developing a text-driven character animation generation system that effectively supports game production pipelines. Our experiments with state-of-the-art motion synthesis models demonstrate significant improvements in both animation quality and style control. The GameMLD dataset and source code can be reached via this link.
