arXiv:2606. 28083v1 Announce Type: cross Abstract: Micro-expression recognition is challenging due to subtle and short-lived facial muscle movements.
arXiv:2406.17538v4 Announce Type: replace Abstract: Micro-expressions are subtle facial movements that occur spontaneously when people try to conceal real emotions. Micro-expression recognition is cr...
arXiv:2609.31285v1 Announce Type: new Abstract: Facial micro-expressions are spontaneous, brief, and subtle facial movements that reveal suppressed emotions in high-stakes environments. In contrast t...
arXiv:2608. 12187v1 Announce Type: cross Abstract: Transformer-based methods have achieved strong performance in monocular 3D human pose estimation, but most existing approaches organise spatial and temporal reasoning as separate stages, which may weaken unified spatial-temporal interdependencies inherent in human motion and compress frame-level structural information before temporal modelling.
arXiv:2606.15848v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has shown strong potential for high-fidelity talking head synthesis. However, enabling fine-grained, interpretable, an...
In this paper, we present the solution developed by our team, XInsight Lab, which achieved first place in Track 3 of the 4th EI-MIGA-IJCAI Challenge with a test accuracy of 0. 76923.
arXiv:2609.13255v1 Announce Type: new Abstract: Facial state analysis plays a crucial role in understanding human expressions, psychological modeling, and human computer interaction. Traditional unim...
The paper introduces MiRA, a plug‑in framework that reweights framewise attention in Vision Transformer video models to better capture subtle facial dynamics for expression recognition. MiRA computes frame‑level confidence and intra‑frame concentration from self‑attention maps, redistributing attention toward localized facial cues without adding trainable parameters. Two modes—an exact post‑softmax redistribution and a lightweight flashLite pre‑softmax approximation—are proposed, and experiments on facial expression recognition benchmarks show consistent gains over strong ViT baselines.
The paper introduces SV-GCN, a single-stream multi-feature fusion framework for 3D skeleton-based gait emotion recognition that incorporates temporal invariance. It uses intra-frame relative motion features to remove frame-rate sensitivity and embeds heterogeneous cues at shallow layers for early fusion, avoiding multi-stream complexity. A global mask-guided valid-frame spatio-temporal graph convolution module further enhances robustness to variable-length sequences and differing frame rates, achieving state‑of‑the‑art performance on the E‑Gait dataset and strong generalization across sequence lengths.
arXiv:2604. 08435v2 Announce Type: replace-cross Abstract: It remains challenging to assess driver fatigue from untrimmed videos under constrained computational budgets, due to the difficulty of modeling long-range temporal dependencies in subtle facial expressions.
arXiv:2607. 16322v1 Announce Type: cross Abstract: Micro-gesture recognition demands the detection of fleeting, spatially localized movements that are frequently overwhelmed by dominant static appearances and background noise.
arXiv:2607. 16760v1 Announce Type: cross Abstract: Driver monitoring systems (DMS) increasingly rely on facial cues to infer drowsiness, distraction, and cognitive load in real time.
arXiv:2609.00846v1 Announce Type: new Abstract: The rising prevalence of psychological disorders necessitates effective emotion monitoring, yet current methods relying on facial or physiological sign...