To cover language, image, and video at the same time for different scenarios, a 3D transformer encoder-decoder framework is designed, which can not only deal with videos as 3D data but also adapt to texts and images as 1D and 2D data, respectively.
Based on this observation, we hypothesize that the general architecture of the transformers, instead of the specific token mixer module, is more essential to the model's performance.
Ranked #41 on Semantic Segmentation on ADE20K
We find that one of the main reasons for that is the lack of an effective receptive field in both the inpainting network and the loss function.
Operating systems include many heuristic algorithms designed to improve overall storage performance and throughput.
Finally, evaluation on five inward-facing benchmarks shows that our method matches, if not surpasses, NeRF's quality, yet it only takes about 15 minutes to train from scratch for a new scene.
Specifically, we first train a self-supervised style encoder on the generic artistic dataset to extract the representations of arbitrary styles.
The diversity and complexity of degradations in real-world video super-resolution (VSR) pose non-trivial challenges in inference and training.