A-TPT refines attention rollout to guide augmentations and ensembles for improved VLM robustness on adversarial and clean data in fine-grained tasks.
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PTA adapts VLMs at test time by maintaining and updating class-specific knowledge prototypes from test samples, achieving higher accuracy than cache-based methods with far less speed loss.
CERSA derives low-rank fine-tuning subspaces from SVD principal components that retain 90-95% spectral energy, delivering higher performance than LoRA and other PEFT baselines at substantially lower memory cost across vision, generation, and language tasks.
citing papers explorer
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Towards Fine-Grained Robustness: Attention-Guided Test-Time Prompt Tuning for Vision-Language Models
A-TPT refines attention rollout to guide augmentations and ensembles for improved VLM robustness on adversarial and clean data in fine-grained tasks.
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Prototype-Based Test-Time Adaptation of Vision-Language Models
PTA adapts VLMs at test time by maintaining and updating class-specific knowledge prototypes from test samples, achieving higher accuracy than cache-based methods with far less speed loss.
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CERSA: Cumulative Energy-Retaining Subspace Adaptation for Memory-Efficient Fine-Tuning
CERSA derives low-rank fine-tuning subspaces from SVD principal components that retain 90-95% spectral energy, delivering higher performance than LoRA and other PEFT baselines at substantially lower memory cost across vision, generation, and language tasks.