English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Pre-training & fine-tuning can enhance the transferring efficiency andperformance in visual tasks. Recent delta-tuning methods provide more optionsfor visual classification tasks. Despite their success, existing visualdelta-tuning art fails to exceed the upper limit of full fine-tuning onchallenging tasks like instance segmentation and semantic segmentation. To finda competitive alternative to full fine-tuning, we propose the Multi-cognitiveVisual Adapter (Mona) tuning, a novel adapter-based tuning method. First, weintroduce multiple vision-friendly filters into the adapter to enhance itsability to process visual signals, while previous methods mainly rely onlanguage-friendly linear filters. Second, we add the scaled normalization layerin the adapter to regulate the distribution of input features for visualfilters. To fully demonstrate the practicality and generality of Mona, weconduct experiments on multiple representative visual tasks, including instancesegmentation on COCO, semantic segmentation on ADE20K, object detection onPascal VOC, and image classification on several common datasets. Excitingresults illustrate that Mona surpasses full fine-tuning on all these tasks andis the only delta-tuning method outperforming full fine-tuning on instancesegmentation and semantic segmentation tasks. For example, Mona achieves a 1%performance gain on the COCO dataset compared to full fine-tuning.Comprehensive results suggest that Mona-tuning is more suitable for retainingand utilizing the capabilities of pre-trained models than full fine-tuning. Thecode will be released at https://github.com/Leiyi-Hu/mona.

Adapter is All You Need for Tuning Visual Tasks