Does the selection of optimizer algorithms affect the robustness of vision-language models against adversarial

Abstract

Abstract: Vision-Language Model (VLM) have gained widespread adoption in Open-Vocabulary (OV) object detection and segmentation tasks. Despite they have shown promise on OV-related tasks, their effectiveness in conventional vision tasks has thus far been unevaluated. In this work, we present the systematic review of VLM-based detection and segmentation, view VLM as the foundational model and conduct comprehensive evaluations across multiple downstream tasks for the first time: 1) The evaluation spans eight detection scenarios (closed-set detection, domain adaptation, crowded objects, etc.) and eight seg

Research Question

Does the selection of optimizer algorithms affect the robustness of vision-language models against adversarial perturbations in instance segmentation benchmarks?

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