Comparison of ST adapter energy-saving and cost-effective performance

GitHub

The adapter architecture is described as (# adapter layers x # bottleneck channels). This is a reproduced code, so the accuracy of the checkpoints may slightly differ from the numbers reported in

High Power Slim Adapter SMPS Solutions

It provides demo board data showing the solutions achieve over 88% and 91% average efficiency at 115V and 230V respectively. Synchronous rectification using

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

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

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

Summary: This paper proposes a new Spatio-Temporal Adapter (ST-Adapter) for parameter-efficient fine-tuning on video tasks. With a much smaller trainable parameter, ST-Adapter

Parameter-Efficient Image-to-Video Transfer Learning

In this work, we investigate such a novel cross-modality transfer learning setting, namely parameter-efficient image-to-video transfer learning. To solve this problem, we propose a new...

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

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

Revaluing the costs and benefits of energy efficiency: A systematic

To enhance the decision-making process of the concerned parties with evidence-based comprehensive tools, we perform a literature review on the costs and benefits associated with energy

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 | Proceedings of the 36th International Conference on

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

Abstract

us work. 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

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

For each entry, we report the top1 action recognition accuracy and the number of fine-tuned parameters. All methods introduce extra parameters beside parameters of the ViT backbone and linear classifier.

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