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cs.CV2025

From Semantics, Scene to Instance-awareness: Distilling Foundation Model for Grounded Open-vocabulary Situation Recognition

Chen Cai, Tianyi Liu, Jianjun Gao +5

Recent Multimodal Large Language Models (MLLMs) exhibit strong zero-shot abilities but struggle with complex Grounded Situation Recognition (GSR) and are resource-intensive for edg…

cs.CV2024

CL-HOI: Cross-Level Human-Object Interaction Distillation from Vision Large Language Models

Jianjun Gao, Chen Cai, Ruoyu Wang +4

Human-object interaction (HOI) detection has seen advancements with Vision Language Models (VLMs), but these methods often depend on extensive manual annotations. Vision Large Lang…

cs.CV2024

MultiFuser: Multimodal Fusion Transformer for Enhanced Driver Action Recognition

Ruoyu Wang, Wenqian Wang, Jianjun Gao +3

Driver action recognition, aiming to accurately identify drivers' behaviours, is crucial for enhancing driver-vehicle interactions and ensuring driving safety. Unlike general actio…

cs.CV2024

CM2-Net: Continual Cross-Modal Mapping Network for Driver Action Recognition

Ruoyu Wang, Chen Cai, Wenqian Wang +4

Driver action recognition has significantly advanced in enhancing driver-vehicle interactions and ensuring driving safety by integrating multiple modalities, such as infrared and d…

cs.CV2024

Multi-modality action recognition based on dual feature shift in vehicle cabin monitoring

Dan Lin, Philip Hann Yung Lee, Yiming Li +4

Driver Action Recognition (DAR) is crucial in vehicle cabin monitoring systems. In real-world applications, it is common for vehicle cabins to be equipped with cameras featuring di…