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20242026
most citedAdaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning

1 citations · 1 across the 5 of their papers we have counts for

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cs.CV2026

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

Xu Li, Yi Zheng, Mengyang Zhao +7

Large Vision-Language Models (LVLMs) typically require processing hundreds to thousands of visual tokens, leading to substantial inference overhead. Existing visual token pruning m…

cs.CV2026

Robust Embodied Perception in Dynamic Environments via Disentangled Weight Fusion

Juncen Guo, Xiaoguang Zhu, Jingyi Wu +4

Embodied perception systems face severe challenges of dynamic environment distribution drift when they continuously interact in open physical spaces. However, the existing domain i…

cs.CV2025

Cross-channel Perception Learning for H&E-to-IHC Virtual Staining

Hao Yang, JianYu Wu, Run Fang +7

With the rapid development of digital pathology, virtual staining has become a key technology in multimedia medical information systems, offering new possibilities for the analysis…

cs.CV20251 cited

Adaptive Weighted Parameter Fusion with CLIP for Class-Incremental Learning

Juncen Guo, Xiaoguang Zhu, Liangyu Teng +4

Class-incremental Learning (CIL) enables the model to incrementally absorb knowledge from new classes and build a generic classifier across all previously encountered classes. When…

cs.CV2025

CalFuse: Multi-Modal Continual Learning via Feature Calibration and Parameter Fusion

Juncen Guo, Siao Liu, Xiaoguang Zhu +8

With the proliferation of multi-modal data in large-scale visual recognition systems, enabling models to continuously acquire knowledge from evolving data streams while preserving…

cs.CV2024

Privacy-Preserving Video Anomaly Detection: A Survey

Yang Liu, Siao Liu, Xiaoguang Zhu +7

Video Anomaly Detection (VAD) aims to automatically analyze spatiotemporal patterns in surveillance videos collected from open spaces to detect anomalous events that may cause harm…