Falls require rapid assistance, but fall-like activities and domain shift complicate alarm control. We present a cross-dataset fall-detection framework combining red–green–blue (RGB) appearance, pose evidence and sequential decisions. A frozen YOLO26n-Pose (You Only Look Once) front end supports 95-dimensional pose/kin...
Behavioral benchmarking reveals \emph{what} biases exist in vision-language models but not \emph{which internal components} are most sensitive to targeted intervention, precluding principled intervention. We argue for mechanism-level evaluation as a necessary complement, demonstrating causal mediation analysis as a dia...
Zhi-Peng Zhao, Wen-Xu Wang, Peishun Liu et al.· 0 citations
Gender bias in large vision-language models (LVLMs) undermines their fairness and reliability, compromising output trustworthiness. Current mitigation methods rely on training-phase adjustments or post-hoc calibration, but face limitations in dynamic visual bias mitigation. These include inability to capture real-time...
Zhi-Peng Zhao, Zhao Wei, Peishun Liu et al.· 0 citations
When strong multimodal models are widely available, progress requires new scientific methodologies beyond benchmark scores---using models as instruments for understanding behavior. We address this by asking: can we use large vision-language models (LVLMs) as experimental instruments for studying their own failure dynam...
Zhi-Peng Zhao, Wen-Xu Wang, Peishun Liu et al.· 0 citations
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