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ST Adapter Upgrade Version and Performance Comparison

arXiv:2312.02238v3 [cs.CV] 23 Apr 2024

s of X-Adapter and the upgraded model. Thanks to our strategies, X-Adapter demonstrates universal compatibility with vari-ous plugins and also enables plugins of different versions to work together,

ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning

we also show the performance impact of using fewer ST-Adapters. As shown in Table 5b, while more ST-Adapters tend to do better, ST-Adapters at deeper layers boost

ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning

Extensive experiments on video action recognition tasks show that our ST-Adapter can match or even outperform the strong full fine-tuning strategy and state-of-the-art video models, whilst enjoying the

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ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning

With a built-in spatio-temporal reasoning capability in a compact design, ST-Adapter enables a pre-trained image model without temporal knowledge to reason about dynamic video

directory-list-2.4.txt/directory-list-2.4.txt at main

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Overview of ST-LINK derivatives

ST-LINK is also the part number of the first implementation of this probe (now obsolete), which is further called ST-LINK/V1 in this document to avoid confusion. This technical note provides an overview of

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The several ST-LINK coexisting versions result from the incremental addition of new functionalities over time, starting from the first ST-LINK/V1 version. This section presents a brief history of the

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NeurIPS23_ST-Adapter_poster

4. Ablation Study on Efficiency The same ViT-B/16 with CLIP pre-training is used for all experiments. Models & source code: https://github /linziyi96/st-adapter

ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning

By default, we insert a ST-Adapter to every Transformer block in the backbone, but we also show the performance impact of using fewer ST-Adapters. As shown in Table 5b, while more ST-Adapters

Solved: St-api patch upgrade

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