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Found 7 papers, 5 papers with code
Memory-Space Visual Prompting for Efficient Vision-Language Fine-Tuning
Current solutions for efficiently constructing large vision-language (VL) models follow a two-step paradigm: projecting the output of pre-trained vision encoders to the input space of pre-trained language models as visual prompts; and then transferring the models to downstream VL tasks via end-to-end parameter-efficient fine-tuning (PEFT).
Revisiting the Parameter Efficiency of Adapters from the Perspective of Precision Redundancy
Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-trained vision models.
Detachedly Learn a Classifier for Class-Incremental Learning
In continual learning, model needs to continually learn a feature extractor and classifier on a sequence of tasks.
FacT: Factor-Tuning for Lightweight Adaptation on Vision Transformer
Recent work has explored the potential to adapt a pre-trained vision transformer (ViT) by updating only a few parameters so as to improve storage efficiency, called parameter-efficient transfer learning (PETL).
Convolutional Bypasses Are Better Vision Transformer Adapters
The pretrain-then-finetune paradigm has been widely adopted in computer vision.
Bypassing Logits Bias in Online Class-Incremental Learning with a Generative Framework
In our framework, a generative classifier which utilizes replay memory is used for inference, and the training objective is a pair-based metric learning loss which is proven theoretically to optimize the feature space in a generative way.
Alleviating Representational Shift for Continual Fine-tuning
We study a practical setting of continual learning: fine-tuning on a pre-trained model continually.
