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Xinyue Yu, Youqing Fang, Pingyu Wu, Guoyang Ye, Wenbo Zhou, Weiming Zhang, Song Xiao

Generating expressive and controllable human speech is one of the core goals of generative artificial intelligence, but its progress has long been constrained by two fundamental challenges: the deep entanglement of speech factors and the coarse granularity of existing control mechanisms. To overcome these challenges, we have proposed a novel framework called MF-Speech, which consists of two core components: MF-SpeechEncoder and MF-SpeechGenerator. MF-SpeechEncoder acts as a factor purifier, adopting a multi-objective optimization strategy to decompose the original speech signal into highly pure and independent representations of content, timbre, and emotion. Subsequently, MF-SpeechGenerator functions as a conductor, achieving precise, composable and fine-grained control over these factors through dynamic fusion and Hierarchical Style Adaptive Normalization (HSAN). Experiments demonstrate that in the highly challenging multi-factor compositional speech generation task, MF-Speech significantly outperforms current state-of-the-art methods, achieving a lower word error rate (WER=4.67%), superior style control (SECS=0.5685, Corr=0.68), and the highest subjective evaluation scores (nMOS=3.96, sMOS_t=3.86, sMOS_e=3.78). Furthermore, the learned discrete factors exhibit strong transferability, demonstrating their significant potential as a general-purpose speech representation.

Longlong Yu, Wenxi Li, Yaoqi Sun, Hang Xu, Chenggang Yan, Yuchen Guo

Despite recent progress in adapting State Space Models such as Mamba to vision tasks, their intrinsic 1D scanning mechanism imposes limitations when applied to inherently 2D-structured data like images. Existing adaptations, including VMamba and 2DMamba, either suffer from inconsistency between scanning order and spatial locality or restrict inter-patch communication to singular paths, hindering effective information propagation. In this paper, we propose 2D-CrossScan, a novel 2D-compatible scan framework that enables spatially consistent, multi-path hidden state propagation by integrating modified state equations over two-dimensional neighborhoods. Furthermore, we mitigate redundant information accumulation due to overlapping paths via cross-directional subtraction. To fully align with the 2D spatial structure, we introduce a multi-directional scanning strategy that starts simultaneously from all four corners of the image, enabling diverse propagation paths and better feature integration. Our approach maintains efficiency, requiring only minimal architectural changes to existing Mamba variants. Experimental results demonstrate substantial improvements in multiple visual tasks, including object detection and semantic segmentation on PANDA and COCO datasets. Compared to baseline SSM-based methods, 2D-CrossScan consistently yields better spatial representations, as confirmed by extensive effective receptive field visualizations and attention analyses. These results highlight the importance of geometry-aware state propagation and validate 2D-CrossScan as a simple yet powerful extension to SSMs for vision.

Wen Yin, Siyu Zhan, Cencen Liu, Xin Hu, Guiduo Duan, Xiurui Xie, Yuan-Fang Li, Tao He

Multimodal Emotion Recognition (MER) aims to accurately identify human emotional states by integrating heterogeneous modalities such as visual, auditory, and textual data. Existing approaches predominantly rely on unified emotion labels to supervise model training, often overlooking a critical challenge: inter-modal emotion conflicts, wherein different modalities within the same sample may express divergent emotional tendencies. In this work, we address this overlooked issue by proposing a novel framework, Typicality-based Consistent-aware Multimodal Emotion Recognition (TiCAL), inspired by the stage-wise nature of human emotion perception. TiCAL dynamically assesses the consistency of each training sample by leveraging pseudo unimodal emotion labels alongside a typicality estimation. To further enhance emotion representation, we embed features in a hyperbolic space, enabling the capture of fine-grained distinctions among emotional categories. By incorporating consistency estimates into the learning process, our method improves model performance, particularly on samples exhibiting high modality inconsistency. Extensive experiments on benchmark datasets, e.g, MOSEI and MER2023, validate the effectiveness of TiCAL in mitigating inter-modal emotional conflicts and enhancing overall recognition accuracy, e.g., with about 2.6% improvements over the state-of-the-art DMD.

Zipeng Ye, Wenjian Luo, Qi Zhou, Yubo Tang

Large language models (LLMs) have seen remarkable growth in recent years. To leverage convenient LLM cloud services, users are inevitably to upload their prompts. Additionally, for tasks such as translation, reading comprehension, and summarization, associated files or context are inherently needed, whether or not they contain user privacy information. Despite the rapid progress in LLM capabilities, research on preserving user privacy during inference has been relatively scarce. To this end, this paper conducts some exploratory research in this domain. Firstly, we show that (1) the embedding space of tokens is highly sparse, and (2) LLMs primarily function in the orthogonal subspace of embedding space, these two factors making privacy extremely vulnerable. Then, we analyze the structural characteristics of LLMs and design a distributed privacy-preserving inference paradigm which can effectively resist privacy attacks. Finally, we perform a thorough evaluation of the defended models on mainstream tasks and find that low-bit quantization techniques can be effectively combined with our inference paradigm, achieving a balance between privacy, utility, and runtime memory efficiency.

Qilang Ye, Yu Zhou, Lian He, Jie Zhang, Xuanming Guo, Jiayu Zhang, Mingkui Tan, Weicheng Xie, Yue Sun, Tao Tan 等

Large Language Models (LLMs) hold rich implicit knowledge and powerful transferability. In this paper, we explore the combination of LLMs with the human skeleton to perform action classification and description. However, when treating LLM as a recognizer, two questions arise: 1) How can LLMs understand the skeleton? 2) How can LLMs distinguish among actions? To address these problems, we introduce a novel paradigm named learning Skeleton representation with visual-motion knowledge for Action Recognition (SUGAR). In our pipeline, we first utilize off-the-shelf large-scale video models as a knowledge base to generate visual, motion information related to actions. Then, we propose to supervise skeleton learning through this prior knowledge to yield discrete representations. Finally, we use the LLM with untouched pre-training weights to understand these representations and generate the desired action targets and descriptions. Notably, we present a Temporal Query Projection (TQP) module to continuously model the skeleton signals with long sequences. Experiments on several skeleton-based action classification benchmarks demonstrate the efficacy of our SUGAR. Moreover, experiments on zero-shot scenarios show that SUGAR is more versatile than linear-based methods.

Sicheng Yang, Yukai Huang, Weitong Cai, Shitong Sun, You He, Jiankang Deng, Hang Zhang, Jifei Song, Zhensong Zhang

The performance of egocentric AI agents is fundamentally limited by multimodal intent ambiguity. This challenge arises from a combination of underspecified language, imperfect visual data, and deictic gestures, which frequently leads to task failure. Existing monolithic Vision-Language Models (VLMs) struggle to resolve these multimodal ambiguous inputs, often failing silently or hallucinating responses. To address these ambiguities, we introduce the Plug-and-Play Clarifier, a zero-shot and modular framework that decomposes the problem into discrete, solvable sub-tasks. Specifically, our framework consists of three synergistic modules: (1) a text clarifier that uses dialogue-driven reasoning to interactively disambiguate linguistic intent, (2) a vision clarifier that delivers real-time guidance feedback, instructing users to adjust their positioning for improved capture quality, and (3) a cross-modal clarifier with grounding mechanism that robustly interprets 3D pointing gestures and identifies the specific objects users are pointing to. Extensive experiments demonstrate that our framework improves the intent clarification performance of small language models (4-8B) by approximately 30%, making them competitive with significantly larger counterparts. We also observe consistent gains when applying our framework to these larger models. Furthermore, our vision clarifier increases corrective guidance accuracy by over 20%, and our cross-modal clarifier improves semantic answer accuracy for referential grounding by 5%. Overall, our method provides a plug-and-play framework that effectively resolves multimodal ambiguity and significantly enhances user experience in egocentric interaction.

Hang Yang, Zhiwu Li, Witold Pedrycz

Crowdsourcing is a common approach for training data-hungry models by collecting high-quality labeled data with human labor. With crowdsourcing data, the end-to-end learning paradigm is rising, where the classifier is concatenated with annotator-specific confusion layers and the two parts are co-trained in a parameter-coupled manner. However, learning with the size of a very large set of annotations is a challenge when computation or energy is limited. In this paper, we analyze and refine the coresets for end-to-end learning from crowds under the sensitivity sampling framework. This coreset is a small possible subset of annotations, so one can efficiently optimize the Coupled Cross-Entropy Minimization problem with guaranteed approximation. We first prove the lower bound, which shows no coresets smaller than complete data with confusion layers. Then, with workers' transition matrices, we show that with the regularization term, this lower bound can be prevented. Our main result is that under mild assumptions, a smaller coreset exists for the regularized Coupled Cross-Entropy Minimization problem. An upper bound of sensitivity is proposed for designing a sampling algorithm called CrowdCore. The experimental results on synthetic and real-world datasets demonstrate the effectiveness of our analysis.

Liqi Yan, Yihao Wu, Chenyi Xu, Chao Yang, Jianhui Zhang, Pan Li

Augmented Reality (AR) navigation has emerged as a transformative tool for spatial intelligence, enabling users to interactively explore complex environments through wearable and mobile AR devices. However, current AR navigation systems struggle with low indoor localization accuracy, weak semantic understanding, and limited long-term memory, which severely limits their adaptability in dynamic, multi-floor, and large-scale real-world settings. To address these challenges, we present AR-Nav benchmark, a novel dataset with corresponding suite that leverages vision and language for AR navigation. First, to construct this benchmark, we proposed an Augmented Reality Visual-Language Memory Model (AR‑VLM²), which generates structured, semantically rich, and temporally indexed representations for long-term AR navigation. Second, we design a lightweight navigation intent recommending module with hierarchical topological reasoning and language-grounded path planning, called ARN‑Pilot, enabling low-latency and personalized route selection. Third, we introduce a closed-loop AR interaction module that supports real-time multi-modal feedback, dynamic memory updates, and human-in-the-loop query refinement. Extensive experiments in indoor multi-floor and outdoor parking scenarios show that AR-Nav suite significantly outperforms state-of-the-art AR navigation methods.

Hongbin Xu, Chaohui Yu, Feng Xiao, Jiazheng Xing, Hai Ci, Weitao Chen, Fan Wang, Ming Li

Despite the remarkable progress of 3D generation, achieving controllability, i.e., ensuring consistency between generated 3D content and input conditions like edge and depth, remains a significant challenge. Existing methods often struggle to maintain accurate alignment, leading to noticeable discrepancies. To address this issue, we propose CyC3D, a new framework that enhances controllable 3D generation by explicitly encouraging cyclic consistency between the second-order 3D content, generated based on extracted signals from the first-order generation, and its original input controls. Specifically, we employ an efficient feed-forward backbone that can generate a 3D object from an input condition and a text prompt. Given an initial viewpoint and a control signal, a novel view is rendered from the generated 3D content, from which the extracted condition is used to regenerate the 3D content. This re-generated output is then rendered back to the initial viewpoint, followed by another round of control signal extraction, forming a cyclic process with two consistency constraints. View consistency ensures coherence between the two generated 3D objects, measured by semantic similarity to accommodate generative diversity. Condition consistency aligns the final extracted signal with the original input control, preserving structural or geometric details throughout the process. Extensive experiments on popular benchmarks demonstrate that CyC3D significantly improves controllability, especially for fine-grained details, outperforming existing methods across various conditions (e.g., +14.17% PSNR for edge, +6.26% PSNR for sketch).

Jian Xiong, Lingxia Jiang, Xianzhong Long, Miaohui Wang, Hao Gao

Point cloud quality assessment (PCQA) is essential for reliable 3D visual applications. While point-based methods face challenges in characterizing distortions due to point cloud disorder, projection-based approaches offer better efficiency but suffer from geometric distortion insensitivity and texture representation blind spots. This study proposes SAF-Net, a multi-view structure-aware feature fusion network for PCQA. We first identify two key limitations in projection-based methods: insufficient geometric distortion perception and representation blind spots (RBS) in texture images. To address these issues, SAF-Net innovatively integrates object mask maps and local binary pattern (LBP) maps. The mask maps enhance geometric distortion perception by extracting edge sharpness and curvature variations, while LBP maps capture essential structural information to overcome RBS and align with human visual system (HVS) sensitivity. SAF-Net employs a hybrid CNN-ViT architecture to balance local feature extraction and global context modeling, along with a progressive fusion strategy to optimize cross-modal feature interaction. Extensive experiments demonstrate the superior performance of SAF-Net on multiple benchmarks, establishing new state-of-the-art results in PCQA.

Xiaohan Xie, Haoran Yu, Biying Shou, Jianwei Huang

What if machines could discover human behavioral patterns better than experts? Traditional behavioral modeling in economics depends on costly manual refinement by domain experts, severely limiting scalability and discovery potential. We introduce AutoBM, an automated behavioral modeling framework leveraging large language models (LLMs) to systematically generate, evaluate, and refine interpretable behavioral models directly from human behavior data. AutoBM represents candidate models as structured natural language specifications, explicitly defining symbolic terms along with their tunable parameters, interpretations, and design rationales. AutoBM leverages LLMs to automatically translate each language specification into executable code, optimize tunable parameters, and evaluate model performance. Utilizing LLM-guided search strategies, AutoBM iteratively recombines and improves models at the term level, closely mirroring human expert practices. Experiments conducted across three distinct strategic environments (the ultimatum game, repeated rock-paper-scissors, and continuous double auctions) demonstrate that AutoBM-generated models consistently outperform leading manually crafted models, achieving significant improvements in prediction accuracy while maintaining clear interpretability. Our results demonstrate that automated frameworks can not only match but systematically exceed human expertise in behavioral modeling, fundamentally changing how we understand strategic human behavior.

Weihao Xia, Cengiz Oztireli

Existing evaluation protocols for brain visual decoding predominantly rely on coarse metrics that obscure inter-model differences, lack neuroscientific foundation, and fail to capture fine-grained visual distinctions. To address these limitations, we introduce BASIC, a unified, multigranular evaluation framework that jointly quantifies structural fidelity, inferential alignment, and contextual coherence between decoded and ground-truth images. For the structural level, we introduce a hierarchical suite of segmentation-based metrics, including foreground, semantic, instance, and component masks, anchored in granularity-aware correspondence across mask structures. For the semantic level, we extract structured scene representations encompassing objects, attributes, and relationships using multimodal large language models, enabling detailed, scalable, and context-rich comparisons with ground-truth stimuli. We benchmark a diverse set of visual decoding methods across multiple stimulus-neuroimaging datasets within this unified evaluation framework. Together, these criteria provide a more discriminative, interpretable, and comprehensive foundation for evaluating brain visual decoding methods.

Lukun Wu, Jie Li, Ziqi Ren, Kaifan Zhang, Xinbo Gao

Decoding visual features from EEG signals is a central challenge in neuroscience, with cross-modal alignment as the dominant approach. We argue that the relationship between visual and brain modalities is fundamentally asymmetric, characterized by two critical gaps: a Fidelity Gap (stemming from EEG's inherent noise and signal degradation, vs. vision's high-fidelity features) and a Semantic Gap (arising from EEG's shallow conceptual representation, vs. vision's rich semantic depth). Previous methods often overlook this asymmetry, forcing alignment between the two modalities as if they were equal partners and thereby leading to poor generalization. To address this, we propose the adaptive teaching paradigm. This paradigm empowers the ``teacher" modality (vision) to dynamically shrink and adjust its knowledge structure under task guidance, tailoring its semantically dense features to match the ``student" modality (EEG)'s capacity. We implement this paradigm with the ShrinkAdapter, a simple yet effective module featuring a residual-free design and a bottleneck structure. Through extensive experiments, we validate the underlying rationale and effectiveness of our paradigm. Our method achieves a top-1 accuracy of 60.2% on the zero-shot brain-to-image retrieval task, surpassing previous state-of-the-art methods by a margin of 9.8%. Our work introduces a new perspective for asymmetric alignment: the teacher must shrink and adapt to bridge the vision-brain gap.

Weining Weng, Yang Gu, Yuan Ma, Yuchen Liu, Yingwei Zhang, Yiqiang Chen

Cross-subject EEG decoding remains a fundamental challenge due to substantial inter-subject variability in brain activity, which hinders the development of subject-independent EEG models. Despite progress in extracting cross-subject invariant features, existing studies neglect the shared neural responses that arise under similar cognitive or emotional states across individuals, limiting their ability to learn generalized and consistent EEG representations. To address the challenges, we propose State Mamba, a novel spatiotemporal EEG state-space model that explicitly models and aligns neural responses and their spatiotemporal state transitions to learn consistent and generalizable representations across subjects. Innovatively, State Mamba theoretically formulates a multi-channel Mamba architecture that jointly models spatial and temporal brain state transitions, supporting principled analysis of neural responses. To enhance spatiotemporal feature coupling, we introduce the LGANN module, which adopts global-local attention to integrate long- and short-term brain activity into a compact EEG representation. Furthermore, we design two self-supervised pretext tasks to extract consistent neural patterns across subjects: (1) representation alignment to align EEG representation, and (2) pattern alignment to align their transition rules under identical conditions, jointly promoting subject-invariant EEG representations. Extensive experiments on three benchmark datasets, FACED, DEAP, and ISRUC, demonstrate the superior performance of State Mamba in cross-subject emotion and sleep recognition tasks, validating its robust generalization capability.

Wenya Wei, Sipeng Yang, Qixian Zhou, Ruochen Liu, Xuelei Zhang, Yifu Yuan, Yan Jiang, Yongle Luo, Hailong Wang, Tianzhou Wang 等

In cooperative video games, traditional AI companions are deployed to assist players, who control them using hotkeys or command wheels to issue predefined commands such as ''attack'', ''defend'', or ''retreat''. Despite their simplicity, these methods, which lack target specificity, limit players' ability to give complex tactical instructions and hinder immersive gameplay experiences. To address this, we propose the FPS AI Companion who Understands Language (F.A.C.U.L.), the first real-time AI system that enables players to communicate and collaborate with AI companions using natural language. By integrating natural language processing with a confidence-based framework, F.A.C.U.L. efficiently decomposes complex commands and interprets player intent. It also employs a dynamic entity retrieval method for environmental awareness, aligning human intentions with decision-making. Unlike traditional rule-based systems, our method supports real-time language interactions, enabling players to issue complex commands such as ''clear the second floor,'' ''take cover behind that tree,'' or ''retreat to the river''. The system provides real-time behavioral responses and vocal feedback, ensuring seamless tactical collaboration. Using the popular FPS game Arena Breakout: Infinite as a case study, we present comparisons demonstrating the efficacy of our approach and discuss the advantages and limitations of AI companions based on real-world user feedback.

Yuxiao Wang, Yu Lei, Wolin Liang, Weiying Xue, Zhenao Wei, Nan Zhuang, Qi Liu

People control their bodies to establish contact with the environment. To comprehensively understand actions across diverse visual contexts, it is essential to simultaneously consider what action is occurring and where it is happening. Current methodologies, however, often inadequately capture this duality, typically failing to jointly model both action semantics and their spatial contextualization within scenes. To bridge this gap, we introduce a novel vision task that simultaneously predicts high-level action semantics and fine-grained body-part contact regions. Our proposed framework, PaIR-Net, comprises three key components: the Contact Prior Aware Module (CPAM) for identifying contact-relevant body parts, the Prior-Guided Concat Segmenter (PGCS) for pixel-wise contact segmentation, and the Interaction Inference Module (IIM) responsible for integrating global interaction relationships. To facilitate this task, we present PaIR (Part-aware Interaction Representation), a comprehensive dataset containing 13,979 images that encompass 654 actions, 80 object categories, and 17 body parts. Experimental evaluation demonstrates that PaIR-Net significantly outperforms baseline approaches, while ablation studies confirm the efficacy of each architectural component.

Yuxiao Wang, Wolin Liang, Yu Lei, Weiying Xue, Nan Zhuang, Qi Liu

Human-Object Interaction (HOI) detection aims to localize human-object pairs and recognize their interactions in images. Although DETR-based methods have recently emerged as the mainstream framework for HOI detection, they still suffer from a key limitation: Randomly initialized queries lack explicit semantics, leading to suboptimal detection performance. To address this challenge, we propose QueryCraft, a novel plug-and-play HOI detection framework that incorporates semantic priors and guided feature learning through transformer-based query initialization. Central to our approach is ACTOR (Action-aware Cross-modal TransfORmer), a cross-modal Transformer encoder that jointly attends to visual regions and textual prompts to extract action-relevant features. Rather than merely aligning modalities, ACTOR leverages language-guided attention to infer interaction semantics and produce semantically meaningful query representations. To further enhance object-level query quality, we introduce a Perceptual Distilled Query Decoder (PDQD), which distills object category awareness from a pre-trained detector to serve as object query initiation. This dual-branch query initialization enables the model to generate more interpretable and effective queries for HOI detection. Extensive experiments on HICO-Det and V-COCO benchmarks demonstrate that our method achieves state-of-the-art performance and strong generalization.

Yuhang Wang, Yanxu Zhu, Jitao Sang

The advanced reasoning capabilities of Large Reasoning Models enable them to thoroughly understand and apply safety policies through deliberate thought processes, thereby improving the models' safety. Beyond safety, these models must also be able to reflect the diverse range of human values across various cultures. This paper presents the Cultural Norm-based Cultural Alignment (CNCA) framework, which enables models to leverage their powerful reasoning ability to align with cultural norms. Specifically, we propose three methods to automatically mine cultural norms from limited survey data and explore ways to effectively utilize these norms for improving cultural alignment. Two alignment paradigms are examined: an in-context alignment method, where cultural norms are explicitly integrated into the user context, and a fine-tuning-based method, which internalizes norms through enhanced Chain-of-Thought training data. Comprehensive experiments demonstrate the effectiveness of these methods, highlighting that models with stronger reasoning capabilities benefit more from cultural norm mining and utilization. Our findings emphasize the potential for reasoning models to better reflect diverse human values through culturally informed alignment strategies.

Yifan Wang, Der-Horng Lee, Bruce X.B. Yu

Prevalent pre-training strategies for Brain-Computer Interfaces (BCIs) are often constrained by spatio-temporal entanglement. This critical issue arises from processing multi-channel Electroencephalography (EEG) signals as monolithic sequences, which intertwines the signal's temporal dynamics with its spatial topography and hinders the learning of robust and generalizable representations. To address this, we introduce BraSTORM, a framework that explicitly disentangles EEG data into separate temporal and spatial streams at the input level. Two streams are processed by parallel encoders trained with a composite dual-objective: a masked signal reconstruction loss captures fine-grained, intra-modal details, while a cross-modal contrastive loss enforces high-level semantic alignment. Extensive fine-tuning experiments on six benchmarks covering three major BCI downstream tasks—Emotion Recognition, Sleep Staging, and Motor Imagery—demonstrate that BraSTORM achieves state-of-the-art performance. Our findings validate that resolving spatio-temporal entanglement at the input level can be a competitive pre-training framework for the BCI field.

Shibo Wang, Yan Zhao, Shigang Wang, Jian Wei, Shuo Li

In recent years, human-AI cognitive consistency has emerged as a crucial perspective for evaluating the perceptual quality and interpretability of AIGC (Artificial Intelligence Generated Content). This paper proposes a biologically inspired saliency prediction framework that models six core regions of the human visual system—namely V1, V2, V4, MT, LIP, and FEF—using liquid neurons to capture the dynamic saliency features aligned with human gaze behavior. To enable effective alignment between AIGC models and human cognitive mechanisms, we introduce a cross-domain dual-teacher distillation strategy and construct a large-scale multimodal dataset comprising natural images, eye-tracking data, AIGC-generated images, and their corresponding cross-attention maps. Furthermore, we propose HAMCI (Human-AI Mutual Cognitive Index), a novel metric designed to quantitatively assess the spatial and semantic alignment between predicted saliency maps and model attention distributions. The proposed method demonstrates promising performance across various saliency prediction and cognitive alignment tasks, with results comparable to or surpassing recent state-of-the-art methods in several benchmarks. The code and dataset will be released upon acceptance to facilitate future research on cognitively aligned AIGC evaluation.