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Jun-Cen Guo

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2026

Spatial–Temporal Decoupled Invertible Neural Networks for Skeleton-Based Video Anomaly Detection

Skeleton-based video anomaly detection has gained significant attention in security and forensics applications due to its privacy-preserving properties that abstract human motion without capturing identifiable biometric information. However, existing methods typically process spatial pose configurations and temporal mo...

Yang Liu, Lei Zhang, Jun-Cen Guo et al. · 0 citations
#machine learning Preprint Sep 2026

Which Models Work Well Together? Measuring Heterogeneity for LLM Team Selection

The performance ceiling of an LLM team is constrained not only by individual model capabilities, but also by inter-member error resonance and predictive differences. Although heterogeneous teaming is often observed to be effective in practice, existing approaches lack complementarity metrics that are computable, interp...

Liang-Yu Teng, Heng-Song Liu, Jun-Cen Guo et al. · 0 citations
Aug 2026

GAF-Det: gated adaptive fusion detector for lightweight real-time object detection

Gated Adaptive Fusion Detector (GAF-Det), a single-stage detector whose neck replaces fixed-weight concatenation with a Lightweight Gated Feature Fusion (LGFF) module that dynamically computes per-channel fusion weights conditioned on both the semantic and spatial streams, demonstrating that input-adaptive feature fusi...

Xuxu Chu, Wenyang Fan, Yuanyuan Meng et al. · 0 citations
Jul 2026

CRISP: Pre-LLM Yet Text-Driven Visual Token Pruning for Efficient LVLM Inference

CRISP is proposed, a pre-LLM yet text-driven visual token pruning framework that preserves both instruction-relevant evidence and essential scene context that serves as a practical solution for efficient LVLM inference, especially in resource-constrained scenarios.

Xu Li, Yi Zheng, Mengyang Zhao et al. · 1 citation

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