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Jiasong Feng, Ao Ma, Jing Wang, Ke Cao, Zhanjie Zhang

Synthesizing motion-rich and temporally consistent videos remains a challenge in artificial intelligence, especially when dealing with extended durations. Existing text-to-video (T2V) models commonly employ spatial cross-attention for text control, equivalently guiding different frame generations without frame-specific textual guidance. Thus, the model's capacity to comprehend the temporal logic conveyed in prompts and generate videos with coherent motion is restricted. To tackle this limitation, we introduce FancyVideo, an innovative video generator that improves the existing text-control mechanism with the well-designed Cross-frame Textual Guidance Module (CTGM). Specifically, CTGM incorporates the Temporal Information Injector (TII) and Temporal Affinity Refiner (TAR) at the beginning and end of cross-attention, respectively, to achieve frame-specific textual guidance. Firstly, TII injects frame-specific information from latent features into text conditions, thereby obtaining cross-frame textual conditions. Then, TAR refines the correlation matrix between cross-frame textual conditions and latent features along the time dimension. Extensive experiments comprising both quantitative and qualitative evaluations demonstrate the effectiveness of FancyVideo. Our approach achieves state-of-the-art T2V generation results on the EvalCrafter benchmark and facilitates the synthesis of dynamic and consistent videos. Note that the T2V process of FancyVideo essentially involves a text-to-image step followed by T+I2V. This means it also supports the generation of videos from user images, i.e., the image-to-video (I2V) task. A significant number of experiments have shown that its performance is also outstanding.

Zhongjie Duan, Chengyu Wang, Cen Chen, Weining Qian, Jun Huang, Mingyi Jin

With the emergence of diffusion models and the rapid development of image processing, generating artistic images in style transfer tasks has become effortless. However, these impressive image processing approaches face consistency issues in video processing due to the independent processing of each frame. In this paper, we propose a powerful, model-free approach called FastBlend to address the consistency problem in video stylization. FastBlend functions as a post-processor and can be seamlessly integrated with diffusion models to create a robust video stylization pipeline. Based on a patch-matching algorithm, we remap and blend the aligned content across multiple frames, thus compensating for inconsistent content with neighboring frames. Moreover, we propose a tree-like data structure and a specialized loss function, aiming to optimize computational efficiency and visual quality for different application scenarios. Extensive experiments have demonstrated the effectiveness of FastBlend. Compared with both independent video deflickering algorithms and diffusion-based video processing methods, FastBlend is capable of synthesizing more coherent and realistic videos.

Zhongjie Duan, Hong Zhang, Wenmeng Zhou, Cen Chen, Yaliang Li, Yu Zhang, Yingda Chen

Recently, advancements in video synthesis have attracted significant attention. Video synthesis models have demonstrated the practical applicability of diffusion models in creating dynamic visual content. Despite these advancements, the extension of video lengths remains constrained by computational resources. Most existing video synthesis models are limited to generating short video clips. In this paper, we propose a novel post-tuning methodology for video synthesis models, called ExVideo. This approach is designed to enhance the capability of current video synthesis models, allowing them to produce content over extended temporal durations while incurring lower training expenditures. In particular, we design extension strategies across common temporal model architectures respectively, including 3D convolution, temporal attention, and positional embedding. To evaluate the efficacy of our proposed post-tuning approach, we trained ExSVD, an extended model based on Stable Video Diffusion model. Our approach enhances the model's capacity to generate up to 5x its original number of frames, requiring only 1.5k GPU hours of training on a dataset comprising 40k videos. Importantly, the substantial increase in video length doesn't compromise the model's innate generalization capabilities, and the model showcases its advantages in generating videos of diverse styles and resolutions. We have released the source code and the enhanced model publicly.

Geng Chen, Xufeng Jian, Yuchen Chen, Pengfei Ren, Jingyu Wang, Haifeng Sun, Qi Qi, Jing Wang, Jianxin Liao

Precise 3D hand posture is essential for learning musical instruments. Reconstructing highly precise 3D hand gestures enables learners to correct and master proper techniques through 3D simulation and Extended Reality. However, exsiting methods typically rely on precisely calibrated multi-camera systems, which are not easily deployable in everyday environments. In this paper, we focus on calibration-free multi-view 3D hand reconstruction in unconstrained scenarios. Establishing correspondences between multi-view images is particularly challenging without camera extrinsics. To address this, we propose A^3-Net, a multi-level alignment framework that utilizes 3D structural representations with hierarchical geometric and explicit semantic information as alignment proxies, facilitating multi-view feature interaction in both 3D geometric space and 2D visual space. Specifically, we first perfrom global geometric alignment to map multi-view features into a canonical space. Subsequently, we aggregate information into predefined sparse and dense proxies to further integrate cross-view semantics through mutual interaction. Finnaly, we perfrom 2D alignment to align projected 2D visual features with 2D observations. Our method achieves state-of-the-art results in the multi-view 3D hand reconstruction task, demonstrating the effectiveness of our proposed framework.

Francisco Braga, Gilberto Bernardes, Roger B. Dannenberg, Nuno Correia

This paper describes an approach to algorithmic music composition that takes narrative structures as input, allowing composers to create music directly from narrative elements. Creating narrative development in music remains a challenging task in algorithmic composition. Our system addresses this by combining leitmotifs to represent characters, generative grammars for harmonic coherence, and evolutionary algorithms to align musical tension with narrative progression. The system operates at different scales, from overall plot structure to individual motifs, enabling both autonomous composition and co-creation with varying degrees of user control. Evaluation with compositions based on tales demonstrated the system's ability to compose music that supports narrative listening and aligns with its source narratives, while being perceived as familiar and enjoyable.

Alexandre Bonlarron, Florian Régin, Elisabetta De Maria, Jean-Charles Régin

Large Language Models (LLMs) excel at generating fluent text but struggle to enforce external constraints because they generate tokens sequentially without explicit control mechanisms. GenCP addresses this limitation by combining LLM predictions with Constraint Programming (CP) reasoning, formulating text generation as a Constraint Satisfaction Problem (CSP). In this paper, we improve GenCP by integrating Masked Language Models (MLMs) for domain generation, which allows bidirectional constraint propagation that leverages both past and future tokens. This integration bridges the gap between token-level prediction and structured constraint enforcement, leading to more reliable and constraint-aware text generation. Our evaluation on COLLIE benchmarks demonstrates that incorporating domain preview via MLM calls significantly improves GenCP's performance. Although this approach incurs additional MLM calls and, in some cases, increased backtracking, the overall effect is a more efficient use of LLM inferences and an enhanced ability to generate feasible and meaningful solutions, particularly in tasks with strict content constraints.

Ruohan Zong, Yang Zhang, Lanyu Shang, Frank Stinar, Nigel Bosch, Dong Wang

This paper studies a bidirectional human-AI collaborative student performance prediction problem to enhance equitable online education, aligning with the United Nations' Sustainable Development Goal (SDG) of ensuring inclusive and equitable quality education for all. The goal is to leverage collaborative intelligence to generate accurate and fair student outcome predictions from behavioral data, ensuring equitable estimation for underrepresented populations. Current fair AI solutions often fail to mitigate demographic bias in the absence of student demographic data, while human-AI collaborative approaches frequently overlook human cognitive biases, leading to inaccurate predictions. We develop CollabDebias, a novel bidirectional human-AI collaborative framework that utilizes the complementary strengths of AI and humans to mitigate the AI demographic bias and human cognitive bias. To address AI demographic bias, we propose an uncertainty learning-based bias identification method and a reliability-aware human-AI integration approach. To reduce human cognitive bias, we design uncertainty-aware visualization of AI decision area and attention mechanism. Experimental results on an online course demonstrate CollabDebias's effectiveness in improving student performance prediction accuracy and fairness.

Yanchen Zhu, Honghui Zou, Chufan Liu, Yuyu Luo, Yuankai Wu, Yuxuan Liang

efficient and adaptable charging infrastructure. Fixed-location charging stations often suffer from underutilization or congestion due to fluctuating demand, while mobile chargers offer flexibility by relocating as needed. This paper studies the optimal planning and operation of hybrid charging infrastructures that combine both fixed and mobile chargers within urban road networks. We formulate the Hybrid Charging Station Planning and Operation (HCSPO) problem, jointly optimizing the placement of fixed stations and the scheduling of mobile chargers. A charging demand prediction model based on Model Predictive Control (MPC) supports dynamic decision-making. To solve the HCSPO problem, we propose a deep reinforcement learning approach enhanced with heuristic scheduling. Experiments on real-world urban scenarios show that our method improves infrastructure availability—achieving up to 244.4% increase in coverage—and reduces user inconvenience with up to 79.8% shorter waiting times, compared to existing solutions.

Yixuan Zhao, Hongkai Wen, Xingchen Zhang, Man Luo

Accurate demand prediction is crucial for the equitable and sustainable expansion of bike-sharing systems, which help reduce urban congestion, promote low-carbon mobility, and improve transportation access in underserved areas. However, expanding these systems presents societal challenges, particularly in ensuring fair resource distribution and operational efficiency. A major hurdle is the difficulty of demand prediction at new stations, which lack historical usage data and are heavily influenced by the existing network. Additionally, new stations dynamically reshape demand patterns across time and space, complicating efforts to balance supply and accessibility in evolving urban environments. Existing methods model relationships between new and existing stations but often assume static patterns, overlooking how new stations reshape demand dynamics over time and space. To tackle these challenges, we propose a novel demand prediction framework for expanding bike-sharing systems, namely BGM, which leverages dynamic graph modeling to capture the evolving inter-station correlations while accounting for spatial and temporal heterogeneity. Specifically, we develop a knowledge transfer approach that studies the embeddings transformation across existing and new stations through a learnable orthogonal mapping matrix. We further design a gated selecting vector-based feature fusion mechanism to integrate the transferred embeddings and the intrinsic features of stations for precise predictions. Experiments on real-world bike-sharing data demonstrate that BGM outperforms existing methods.

Jinwei Zeng, Yu Liu, Guozhen Zhang, Jingtao Ding, Yuming Lin, Jian Yuan, Yong Li

Accurately estimating high-resolution carbon emissions is crucial for effective emission governance and mitigation planning. While conventional methods for precise carbon accounting are hindered by substantial data collection efforts, the rise of open data and advanced learning techniques offers a promising solution. Once an open data-based prediction model is developed and trained, it can easily infer emissions for new areas based on available open data. To address this, we incorporate two modalities of open data, satellite images and point-of-interest (POI) data, to predict high-resolution urban carbon emissions, with satellite images providing macroscopic and static and POI data offering fine-grained and relatively dynamic functionality information. However, estimating high-resolution carbon emissions presents two significant challenges: the intertwined and implicit effects of various functionalities on carbon emissions, and the complex spatial contiguity correlations that give rise to the agglomeration effect. Our model, OpenCarbon, features two major designs that target the challenges: a cross-modality information extraction and fusion module to extract complementary functionality information from two modules and model their interactions, and a neighborhood-informed aggregation module to capture the spatial contiguity correlations. Extensive experiments demonstrate our model's superiority, with a significant performance gain of 26.6% on R2. Further generalizability tests and case studies also show OpenCarbon's capacity to capture the intrinsic relation between urban functionalities and carbon emissions, validating its potential to empower efficient carbon governance and targeted carbon mitigation planning. Codes and data are available: https://github.com/JinweiZzz/OpenCarbon.

Xiaoyan Yuan, Wei Wang, Han Liu, Jian Chen, Xiping Hu

Self-supervised learning (SSL) has garnered increasing attention in electrocardiogram (ECG) analysis for its effectiveness in resource-limited settings. Existing state-of-the-art SSL methods rely on time-frequency detail reconstruction, but due to the inherent redundancy of ECG signals and individual variability, these approaches often yield suboptimal performance. In contrast, discrete label prediction becomes a superior pre-training objective by encouraging models to efficiently abstract ECG high-level semantics. However, the continuity and significant variability of ECG signals pose a challenge in generating semantically discrete labels. To address this issue, we propose an ECG pretraining framework with a self-distillation semantic tokenizer (ECG2TOK), which maps continuous ECG signals into discrete labels for self-supervised training. Specifically, the tokenizer extracts semantically aware embeddings of ECG by self-distillation and performs online clustering to generate semantically rich discrete labels. Subsequently, the SSL model is trained in conjunction with masking strategies and discrete label prediction to facilitate the abstraction of high-level semantic representations. We evaluate ECG2TOK in six downstream tasks, demonstrating that ECG2TOK efficiently achieves state-of-the-art performance and up to a 30.73% AUC increase in low-resource scenarios. Moreover, visualization experiments demonstrate that the discrete labels generated by ECG2TOK exhibit consistent semantics closely associated with clinical features. Our code is available on https://github.com/YXYanova/ECG2TOK.

Mingruo Yuan, Ben Kao, Tien-Hsuan Wu

Retrieval of legal knowledge by the general public is a challenging problem due to the technicality of the professional knowledge and the lack of fundamental understanding by laypersons on the subject. Traditional information retrieval techniques assume that users are capable of formulating succinct and precise queries for effective document retrieval. In practice, however, the wide gap between the highly technical contents and untrained users makes legal knowledge retrieval very difficult. We propose a methodology, called QBR, which employs a Questions Bank (QB) as an effective medium for bridging the knowledge gap. We show how the QB is used to derive training samples to enhance the embedding of knowledge units within documents, which leads to effective fine-grained knowledge retrieval. We discuss and evaluate through experiments various advantages of QBR over traditional methods. These include more accurate, efficient, and explainable document retrieval, better comprehension of retrieval results, and highly effective fine-grained knowledge retrieval. We also present some case studies and show that QBR achieves social impact by assisting citizens to resolve everyday legal concerns.

Qihao Yang, Yu Yang, Sixu An, Tianyong Hao, Guandong Xu

Large language models (LLMs) fundamentally reshape learning and teaching models, shifting tutoring systems from supporting individual learning to facilitating collaborative learning (CL) like task-oriented group discussions. However, existing AI tutors struggle to guide CL, as they seldom model the interactions between AI tutors and students. Therefore, they cannot scaffold students to complete tasks collaboratively, which impairs learning outcomes and pedagogy adaptability. Additionally, existing AI tutors fail to make use of CL theories to generate instructive feedback, which leads to undesirable interactions such as over-instruction and limits students' autonomy. In this paper, we propose an LLM-based collaborative agent that innovatively leverages pedagogical strategies to sense discussion stages, detect learning issues, identify the timing of intervention, and generate instructive feedback. To develop the agent, we first design a prompting strategy based on a CL theory, that is, the Community of Inquiry, to cultivate the agent to understand the discussion status. Second, a multi-agent interaction framework is proposed to simulate the collaborative learning behavior between AI tutors and students. Meanwhile, a synthetic task-oriented group discussion dataset, namely CLTeach, is generated, which consists of 27k manually-verified multi-party dialogues with fine-grained annotations of instructive feedback and explanations. Lastly, we use CLTeach to fine-tune the LLM agent, ultimately enabling it to generate instructive feedback at the right time to support students in CL. Extensive experiments demonstrate that our agent achieves state-of-the-art performance in feedback generation and has the potential to mimic human teachers effectively.

Tianjiao Xu, Jinfei Gao, Keyi Kong, Jianhua Yin, Tian Gan, Liqiang Nie

Multimodal controversy detection, which involves determining whether a given video and its associated comments are controversial, plays a pivotal role in risk management on social video platforms. Existing methods typically provide only classification results, failing to identify what aspects are controversial and why, thereby lacking detailed explanations. To address this limitation, we propose a novel Agent-based Multimodal Controversy Detection architecture, termed AgentMCD. This architecture leverages Large Language Models (LLMs) as generative agents to simulate human behavior and improve explainability. AgentMCD employs a multi-aspect reasoning process, where multiple judges conduct evaluations from diverse perspectives to derive a final decision. Furthermore, a multi-agent simulation process is incorporated, wherein agents act as audiences, offering opinions and engaging in free discussions after watching videos. This hybrid framework enables comprehensive controversy evaluation and significantly enhances explainability. Experiments conducted on the MMCD dataset demonstrate that our proposed architecture outperforms existing LLM-based baselines in both high-resource and low-resource comment scenarios, while maintaining superior explainability.

Chi Xu, Yili Jin, Sami Ma, Rongsheng Qian, Hao Fang, Jiangchuan Liu, Xue Liu, Edith C.H. Ngai, William I. Atlas, Katrina M. Connors 等

Wild salmon are essential to the ecological, economic, and cultural sustainability of the North Pacific Rim. Yet climate variability, habitat loss, and data limitations in remote ecosystems that lack basic infrastructure support pose significant challenges to effective fisheries management. This project explores the integration of multimodal foundation AI and expert-in-the-loop frameworks to enhance wild salmon monitoring and sustainable fisheries management in Indigenous rivers across Pacific Northwest. By leveraging video and sonar-based monitoring, we develop AI-powered tools for automated species identification, counting, and length measurement, reducing manual effort, expediting delivery of results, and improving decision-making accuracy. Expert validation and active learning frameworks ensure ecological relevance while reducing annotation burdens. To address unique technical and societal challenges, we bring together a cross-domain, interdisciplinary team of university researchers, fisheries biologists, Indigenous stewardship practitioners, government agencies, and conservation organizations. Through these collaborations, our research fosters ethical AI co-development, open data sharing, and culturally informed fisheries management.

Qipeng Xie, Hao Guo, Weizheng Wang, Yongzhi Huang, Linshan Jiang, Jiafei Wu, Shuxin Zhong, Lu Wang, Kaishun Wu

Global aging necessitates tele-monitoring systems to provide real-time tracking and timely assistance for older adults living independently. While pervasive wireless devices (e.g., CSI, IMU, UWB) enable cost-effective, non-intrusive monitoring, existing systems lack flexibility, limiting their adaptability to different environments. In this work, we posit that the motion dynamics of human movement are invariant across sensing modalities, inspiring the design of HARMONY—a privacy-preserving, sensor-agnostic system that supports multi-modal inputs and diverse tele-monitoring tasks. HARMONY incorporates Modality-agnostic Data Processing to uniformly encrypt multi-modal signals and Task-specific Activity Recognition for seamless tasks adaptation. A novel Encrypted-processing Engine then significantly accelerates computations on encrypted data by optimizing matrix and convolution operations. Evaluations across five different sensing modalities show that HARMONY consistently achieves high accuracy while delivering 3.5 × to 130 × speedups over state-of-the-art baselines. Our results demonstrate that HARMONY is a practical, scalable, and privacy-centric prototype for next-generation remote healthcare.

Tianxing Wu, Lizhe Cao, Shuang Wang, Jiming Wang, Shutong Zhu, Yerong Wu, Yuqing Feng

To advance the United Nations Sustainable Development Goal on promoting sustained, inclusive, and sustainable economic growth, foreign direct investment (FDI) plays a crucial role in catalyzing economic expansion and fostering innovation. Precise city-level FDI prediction is quite important for local government and is commonly studied based on economic data (e.g., GDP). However, such economic data could be prone to manipulation, making predictions less reliable. To address this issue, we try to leverage large-scale judicial data which reflects judicial performance influencing local investment security and returns, for city-level FDI prediction. Based on this, we first build an index system for the evaluation of judicial performance over twelve million publicly available adjudication documents according to which a tabular dataset is reformulated. We then propose a new Tabular Learning method on Judicial Data (TLJD) for city-level FDI prediction. TLJD integrates row data and column data in our built tabular dataset for judicial performance indicator encoding, and utilizes a mixture of experts model to adjust the weights of different indicators considering regional variations. To validate the effectiveness of TLJD, we design cross-city and cross-time tasks for city-level FDI predictions. Extensive experiments on both tasks demonstrate the superiority of TLJD (reach to at least 0.92 R2) over the other ten state-of-the-art baselines in different evaluation metrics.

Zheng Wei, Mingchen Li, Pu Zhang, Xinyu Liu, Huamin Qu, Pan Hui

Detecting hidden stigmatization in social media poses significant challenges due to semantic misalignments between textual and visual modalities, as well as the subtlety of implicit stigmatization. Traditional approaches often fail to capture these complexities in real-world, multimodal content. To address this gap, we introduce ContextAware, an agent-based framework that leverages specialized modules to collaboratively process and analyze images, textual context, and social interactions. Our approach begins by clustering image embeddings to identify recurring content, activating high-likes agents for deeper analysis of images receiving substantial user engagement, while comprehensive agents handle lower-engagement images. By integrating case-based learning, textual sentiment, and vision-language models (VLMs), ContextAware refines its detection of harmful content. We evaluate ContextAware on a self-collected Douyin dataset focused on interracial relationships, comprising 871 short videos and 885,502 comments—of which a notable portion are image-based. Experimental results show that ContextAware not only outperforms state-of-the-art methods in accuracy and F1 score but also effectively detects implicit stigmatization within the highly contextual environment of social media. Our findings underscore the importance of agent-based architectures and multimodal alignment in capturing nuanced, culturally specific forms of harmful content.

Bonan Wang, Haicheng Liao, Chengyue Wang, Bin Rao, Yanchen Guan, Guyang Yu, Jiaxun Zhang, Songning Lai, Chengzhong Xu, Zhenning Li

Accurate trajectory prediction has long been a major challenge for autonomous driving (AD). Traditional data-driven models predominantly rely on statistical correlations, often overlooking the causal relationships that govern traffic behavior. In this paper, we introduce a novel trajectory prediction framework that leverages causal inference to enhance predictive robustness, generalization, and accuracy. By decomposing the environment into spatial and temporal components, our approach identifies and mitigates spurious correlations, uncovering genuine causal relationships. We also employ a progressive fusion strategy to integrate multimodal information, simulating human-like reasoning processes and enabling real-time inference. Evaluations on five real-world datasets—ApolloScape, nuScenes, NGSIM, HighD, and MoCAD—demonstrate our model's superiority over existing state-of-the-art (SOTA) methods, with improvements in key metrics such as RMSE and FDE. Our findings highlight the potential of causal reasoning to transform trajectory prediction, paving the way for robust AD systems.

Meng Wan, Tiantian Liu, Yuxuan Bi, Jue Wang, Hui Cui, Rongqiang Cao, Jiaxiang Wang, Peng Shi, Ningming Nie, Yangang Wang

Over 4.15 million low-income households across nearly 60,000 villages in China benefit from photovoltaic (PV) poverty alleviation power stations. However, weak infrastructure and limited capabilities make these systems vulnerable to fluctuations. One of the United Nations' Sustainable Development Goals (SDG 7) seeks to ensure access to affordable and reliable energy for all, especially in underdeveloped regions. This paper proposes MCloudNet, a multi-modal framework designed to improve ultra-short-term PV prediction in data-scarce, cloud-dynamic environments. MCloudNet explicitly models multi-layer cloud structures from satellite imagery and fuses them with time-series meteorological data to enhance prediction accuracy and interpretability. A province-level dispatch system with MCloudNet has been deployed in Hebei, supporting scheduling across rural PV stations. Experiments conducted in counties such as Shexian and Luxi highlight the framework's effectiveness for use in underdeveloped micro-grids. Operational results show that the system has reduced over 60 million kWh of solar curtailment and generated 24 million CNY in economic value, benefiting approximately 50,000 rural households. By minimizing power fluctuations and improving rural energy scheduling, MCloudNet supports essential services such as lighting, medical facilities, and communications. The source code is available at: https://github.com/AI4SClab/MCloudNet.