arXiv subjects

Yuhao Shan

Publications and source records attributed to Yuhao Shan.

3 recordsLinked to original sources

arXiv2026

SAC$^2$-Net: Semantic Anchoring and Complementary-Consensus Fusion for Multimodal Micro-Expression Recognition

Micro-expression recognition (MER) is challenging due to subtle facial movements, limited data, and the ambiguous relationship between Action Units (AUs) and emotion categories. Optical flow and motion magnification are two widely used representations for making subtle facial dynamics observable. However, many existing methods treat them as separate cues or fuse them without explicitly modeling their dual complementarity. Optical flow encodes displacement-level muscle motion, whereas motion magnification reveals appearance-level changes in facial texture and context. When both modalities are informative, their combination provides a more complete characterization of subtle facial dynamics; when one modality degrades, the other may still preserve discriminative evidence for compensation. This dual complementarity provides richer facial representations, but also introduces two key challenges for multimodal fusion: cross-modal heterogeneity and spatially varying modality reliability. To address these challenges, we propose SAC$^2$-Net, a Semantic Anchoring and Complementary-Consensus Network that first aligns heterogeneous visual representations with semantic anchors and then performs reliability-aware complementary fusion. Specifically, Semantic Anchoring Soft Alignment (SASA) converts activated AUs into textual prompts and uses hierarchical AU-aware soft labels to align motion-magnified and optical-flow representations while preserving semantic proximity among anatomically related samples. Based on the aligned representations, Complementary-Consensus Fusion (CCF) exchanges complementary motion and appearance cues, adaptively enhances unreliable local responses with trustworthy cross-modal evidence, and further encourages a shared spatial focus through consensus refinement.

cs.CV↗

arXiv2026

VisionCreator: A Native Visual-Generation Agentic Model with Understanding, Thinking, Planning and Creation

Visual content creation tasks demand a nuanced understanding of design conventions and creative workflows-capabilities challenging for general models, while workflow-based agents lack specialized knowledge for autonomous creative planning. To overcome these challenges, we propose VisionCreator, a native visual-generation agentic model that unifies Understanding, Thinking, Planning, and Creation (UTPC) capabilities within an end-to-end learnable framework. Our work introduces four key contributions: (i) VisGenData-4k and its construction methodology using metacognition-based VisionAgent to generate high-quality creation trajectories with explicit UTPC structures; (ii) The VisionCreator agentic model, optimized through Progressive Specialization Training (PST) and Virtual Reinforcement Learning (VRL) within a high-fidelity simulated environment, enabling stable and efficient acquisition of UTPC capabilities for complex creation tasks; (iii) VisGenBench, a comprehensive benchmark featuring 1.2k test samples across diverse scenarios for standardized evaluation of multi-step visual creation capabilities; (iv) Remarkably, our VisionCreator-8B/32B models demonstrate superior performance over larger closed-source models across multiple evaluation dimensions. Overall, this work provides a foundation for future research in visual-generation agentic systems.

cs.CV↗

arXiv2025

Dual-Projection Fusion for Accurate Upright Panorama Generation in Robotic Vision

Panoramic cameras, capable of capturing a 360-degree field of view, are crucial in robotic vision, particularly in environments with sparse features. However, non-upright panoramas due to unstable robot postures hinder downstream tasks. Traditional IMU-based correction methods suffer from drift and external disturbances, while vision-based approaches offer a promising alternative. This study presents a dual-stream angle-aware generation network that jointly estimates camera inclination angles and reconstructs upright panoramic images. The network comprises a CNN branch that extracts local geometric structures from equirectangular projections and a ViT branch that captures global contextual cues from cubemap projections. These are integrated through a dual-projection adaptive fusion module that aligns spatial features across both domains. To further enhance performance, we introduce a high-frequency enhancement block, circular padding, and channel attention mechanisms to preserve 360{\deg} continuity and improve geometric sensitivity. Experiments on the SUN360 and M3D datasets demonstrate that our method outperforms existing approaches in both inclination estimation and upright panorama generation. Ablation studies further validate the contribution of each module and highlight the synergy between the two tasks. The code and related datasets can be found at: https://github.com/YuhaoShine/DualProjectionFusion.

cs.CV↗