Multi-view action recognition aims to identify action categories from given clues. Existing studies ignore the negative influences of fuzzy views between view and action in disentangling, commonly arising the mistaken recognition results. To this end, we regard the observed image as the composition of the view and action components, and give full play to the advantages of multiple views via the adaptive cooperative representation among these two components, forming a Dual-Recommendation Disentanglement Network (DRDN) for multi-view action recognition. Specifically, 1) For the action, we leverage a multi-level Specific Information Recommendation (SIR) to enhance the interaction among intricate activities and views. SIR offers a more comprehensive representation of activities, measuring the trade-off between global and local information. 2) For the view, we utilize a Pyramid Dynamic Recommendation (PDR) to learn a complete and detailed global representation by transferring features from different views. It is explicitly restricted to resist the fuzzy noise influence, focusing on positive knowledge from other views. Our DRDN aims for complete action and view representation, where PDR directly guides action to disentangle with view features and SIR considers mutual exclusivity of view and action clues. Extensive experiments have indicated that the multi-view action recognition method DRDN we proposed achieves state-of-the-art performance over powerful competitors on several standard benchmarks. The code will be available at https://github.com/51cloud/DRDN.

This paper introduces a new and challenging Hidden Intention Discovery (HID) task. Unlike existing intention recognition tasks, which are based on obvious visual representations to identify common intentions for normal behavior, HID focuses on discovering hidden intentions when humans try to hide their intentions for abnormal behavior. HID presents a unique challenge in that hidden intentions lack the obvious visual representations to distinguish them from normal intentions. Fortunately, from a sociological and psychological perspective, we find that the difference between hidden and normal intentions can be reasoned from multiple micro-behaviors, such as gaze, attention, and facial expressions. Therefore, we first discover the relationship between micro-behavior and hidden intentions and use graph structure to reason about hidden intentions. To facilitate research in the field of HID, we also constructed a seminal dataset containing a hidden intention annotation of a typical theft scenario for HID. Extensive experiments show that the proposed network improves performance on the HID task by 9.9% over the state-of-the-art method SBP.

Comprehensive cognitive-affective profiling for precision ASD assessment is hindered by inadequate computational modeling of emotion-behavior dynamics. This gap persists due to limited methods for reconciling EEG, eye-tracking, and facial expression modalities-critical for capturing ASD heterogeneity drivers. This study proposes the Cognitive-Affective Ability Assessment (CA³) framework, focusing on attention control in conjunction with valence-arousal, to address these limitations through: (1) Ability-Specific Neural Disentanglement extracting attention- and emotion-specific representations from EEG using ET and facial expression recordings as anchors; (2) Symmetrical Cross-Ability Alignment modeling contextual dependencies via a symmetric cross-attention mechanism; and (3) Uncertainty-Aware Ability Integration fusing per-ability predictions using Dirichlet modeling and Dempster-Shafer theory while quantifying confidence. Extensive experiments have been conducted to evaluate CA$^{3}$ vs. state-of-the-arts counterparts on the multimodel datasets (EEG, ET and facial expression recordings) with CCNU (27 ASD vs. 30 typically developing (TD) children) and BNU (49 ASD vs. 48 TD), and the results demonstrate that: 1) In the ASD cognitive-affective abilities assessment task, accuracy is improved by 2.7% for attention control and 2.5% for emotion perception, and by 3.5% for cognitive-affective abilities assessment, compared with current state-of-the-art methods. 2) For attention control and valence-arousal, composite ability gaps are measured between ASD and typically developing children: 15.6% and 19.9% for mild ASD, 28.2% and 39.1% for moderate ASD, and 44.4% and 61.3% for severe ASD, respectively. Overall, the framework effectively bridges computational assessment with clinically actionable insights, enabling robust decision-making through personalized cognitive-affective ASD profiles.

Objective. EEG-based assessment of motor response capability is critical for understanding the heterogeneous cognitive profiles of children with autism spectrum disorder (ASD), e.g. typically P300 event assessment. However, mainstream methods generally struggle to address the entangled trial-to-trial variability and temporal instability of P300 signatures in ASD, fixed time windows or trial-averaging primarily effective only for typically developing subjects. Approach. To overcome this limitation, this paper introduces DynaSPARC ( Dyna mic S ingle-trial P 300 A ssessment of R esponse C apability), a framework based on the premise that behavioral reaction time ( R T ) provides a latent regulation for inferring trial-specific cognitive dynamics. DynaSPARC enables reliable assessment of motor response capability in ASD through: (1) Dynamic Temporal Windowing , which uses a nonlinear mapping of standardized R T differences to adaptively parameterize the start and length of the P300 window for each trial within a physiologically constrained range (e.g. 300–800 ms); (2) Collaborative Spatio-temporal Attention , which employs a learnable temporal filter to pinpoint P300 latency and a knowledge-guided channel weighting scheme for a reliable reconstruction of the P300 signature. Main results. Evaluation on an EEG dataset from 67 children (29 with ASD vs 38 typically developing) through a motor-cognitive task demonstrates that DynaSPARC achieves superior performance over fixed-window methods: (1) A more temporally localized and spatially plausible single-trial P300 was obtained, and a substantially stronger R T -peak-latency coupling was preserved (Spearman ρ = 0.61, p = 1.33 × 10 − 8 vs ρ = 0.11, p = 0.391), consistent with more stable peak-latency estimates (reduced jitter); (2) The peak scalp topography showed a more canonical, spatially focused centro-parietal positivity; (3) The classification accuracy, F1 score, recall, and precision reached 84.77%, 78.43%, 84.03%, and 76.14%, respectively. Significance. This work establishes a new paradigm for EEG-based assessment, moving beyond static averaging to model the dynamic interplay between neural latency and behavioral output.