Direct Manufacturer 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
Direct Manufacturer 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
Direct Manufacturer st-adapter/README.md at main · linziyi96/st-adapter · GitHub
We release the data list we used for Kinetics-400 (k400, train list link, val list link) and Something-something-v2 (ssv2, train list link, val list link), which reflect the class mapping of the released
Direct Manufacturer ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning for
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning for Action Recognition: Paper and Code. Capitalizing on large pre-trained models for various downstream tasks
Direct Manufacturer 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...
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Direct Manufacturer ST-Adapter: Parameter-Efficient Image-to-Video
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
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Direct Manufacturer 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
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Direct Manufacturer Parameter-Efficient Image-to-Video Transfer Learning
fewer updated parameters compared to previous work. Extensive experiments on video action recognition tasks show that our ST-Adapter can match or even outperform the strong full fine-tuning
Direct Manufacturer ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning
Capitalizing on large pre-trained models for various downstream tasks of interest have recently emerged with promising performance. Due to the ever-growing model size, the standard full fine-tuning based
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Direct Manufacturer [2501.08271] Comparative Analysis of Efficient Adapter-Based Fine
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Direct Manufacturer 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
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Abstract Capitalizing on large pre-trained models for various downstream tasks of interest have recently emerged with promising performance. Due to the ever-growing model size, the standard full fine
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Direct Manufacturer ST-Adapter: Parameter-Efficient Image-to-Video Transfer
The experiments show that the ST-Adapter performs as well as or even better than state-of-the-art video models while requiring less training time and computational resources.
Direct Manufacturer 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
Direct Manufacturer 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
Direct Manufacturer NeurIPS23_ST-Adapter_poster
ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning Junting Pan*, Ziyi Lin*, Xiatian Zhu, Jing Shao, Hongsheng Li