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cs.CV2026
Recovering Policy-Induced Errors: Benchmarking and Trajectory Synthesis for Robust GUI Agents
Tianpeng Bu, Xin Liu, Qihua Chen +7
While GUI agents have advanced rapidly, they often lack the robustness to recover from their own errors, hindering real-world deployment. To bridge this gap at both the evaluation…
cs.CV2024
A Lost Opportunity for Vision-Language Models: A Comparative Study of Online Test-Time Adaptation for Vision-Language Models
Mario Döbler, Robert A. Marsden, Tobias Raichle +1
In deep learning, maintaining model robustness against distribution shifts is critical. This work explores a broad range of possibilities to adapt vision-language foundation models…
cs.CV2024
Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation
Mario Döbler, Florian Marencke, Robert A. Marsden +1
Since distribution shifts are likely to occur after a model's deployment and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update…