RobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereo

• URL: http://arxiv.org/abs/2511.10107v1
• Date: Fri, 14 Nov 2025 01:33:08 GMT
• Title: RobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereo
• Authors: Jueun Ko, Hyewon Park, Hyesong Choi, Dongbo Min,
• Abstract summary: RobIA is a novel Robust, Instance-Aware framework for Continual Test-Time Adaptation in stereo depth estimation.<n>RobIA integrates two key components: (1) Attend-and-Excite Mixture-of-Experts (AttEx-MoE), a parameter-efficient module that dynamically routes input to frozen experts via lightweight self-attention mechanism tailored to epipolar geometry, and (2) Robust AdaptBN Teacher, a PEFT-based teacher model that provides dense pseudo-supervision by complementing sparse handcrafted labels.
• Score: 18.836469118006594
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Stereo Depth Estimation in real-world environments poses significant challenges due to dynamic domain shifts, sparse or unreliable supervision, and the high cost of acquiring dense ground-truth labels. While recent Test-Time Adaptation (TTA) methods offer promising solutions, most rely on static target domain assumptions and input-invariant adaptation strategies, limiting their effectiveness under continual shifts. In this paper, we propose RobIA, a novel Robust, Instance-Aware framework for Continual Test-Time Adaptation (CTTA) in stereo depth estimation. RobIA integrates two key components: (1) Attend-and-Excite Mixture-of-Experts (AttEx-MoE), a parameter-efficient module that dynamically routes input to frozen experts via lightweight self-attention mechanism tailored to epipolar geometry, and (2) Robust AdaptBN Teacher, a PEFT-based teacher model that provides dense pseudo-supervision by complementing sparse handcrafted labels. This strategy enables input-specific flexibility, broad supervision coverage, improving generalization under domain shift. Extensive experiments demonstrate that RobIA achieves superior adaptation performance across dynamic target domains while maintaining computational efficiency.

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