Recently, Transformer-based video recognition models have achieved state-of-the-art results on major video recognition benchmarks. However, their high inference cost significantly limits research speed and practical use. In video compression, methods considering small motions and residuals that are...
Compared with the best results that Transformer-based models can offer, PatchTST/64 achieves an overall 21.0% reduction on MSE and 16.7% reduction on MAE, while PatchTST/42 attains a overall 20.2% reduction on MSE and 16.4% reduction on MAE. It also outperforms other non-Transformer-based ...
It also outperforms other non-Transformer-based models like DLinear. Self-supervised Learning We do comparison with other supervised and self-supervised models, and self-supervised PatchTST is able to outperform all the baselines. We also test the capability of transfering the pre-trained model to...
The great success of transformer-based models in natural language processing (NLP) has led to various attempts at adapting these architectures to other domains such as vision and audio. Recent work has shown that transformers can outperform Convolutional Neural Networks (CNNs) on vision and audio ...
Visual language pre-training (VLP) models have demonstrated significant success in various domains, but they remain vulnerable to adversarial attacks. Addressing these adversarial vulnerabilities is crucial for enhancing security in multi-modal learning. Traditionally, adversarial methods that target VLP model...
which are converted into tokens. These tokens represent various essential features of the mesh. Similar blocks are grouped as patches to further reduce the dimensionality of our data. The next step includes feeding this reduced data to a trans...
Due to an overhead perspective and a significantly larger scale in RSIs, a patch-level region-aware module is designed to filter the redundant information in the RSI scene, which benefits the Transformer-based decoder by attaining improved image perception. Technically, the trainable multi-label ...
Received03 March 2024 Accepted27 November 2024 Published16 December 2024 Issue DateFebruary 2025 DOIhttps://doi.org/10.1007/s00521-024-10836-5 Keywords Facial action unit detection Vision transformer Perceiver Sparse landmarksAccess this article Log in via an institution Subscribe and save Springer...
About Code release for "PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting" - PatchMixer/models/Transformer.py at main · tanjingme/PatchMixer
Compared with the best results that Transformer-based models can offer, PatchTST/64 achieves an overall 21.0% reduction on MSE and 16.7% reduction on MAE, while PatchTST/42 attains a overall 20.2% reduction on MSE and 16.4% reduction on MAE. It also outperforms other non-Transformer-based ...