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Found 4 papers, 1 papers with code
Adapter is All You Need for Tuning Visual Tasks
To fully demonstrate the practicality and generality of Mona, we conduct experiments on multiple representative visual tasks, including instance segmentation on COCO, semantic segmentation on ADE20K, object detection on Pascal VOC, and image classification on several common datasets.
Parameter-efficient is not sufficient: Exploring Parameter, Memory, and Time Efficient Adapter Tuning for Dense Predictions
We provide a gradient backpropagation highway for low-rank adapters which eliminates the need for expensive backpropagation through the frozen pre-trained model, resulting in substantial savings of training memory and training time.
1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions
Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard technique for achieving state-of-the-art performance on computer vision benchmarks.
Beyond the Limitation of Monocular 3D Detector via Knowledge Distillation
Moreover, we construct a depth-guided matrix by the predicted depth gap of teacher and student to facilitate the model to learn more knowledge of farther objects in prediction level distillation.
