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This paper treats visual representation learning generally as a sequence-to-sequence prediction task, and forms a family of Hierarchical Local-Global (HLG) ...
Jul 19, 2022 · In this paper, we aim to provide an alternative perspective by treating visual representation learning generally as a sequence-to-sequence ...
Jul 19, 2022 · In this paper, we aim to provide an alternative perspective by treating visual representation learning generally as a sequence-to-sequence ...
Revisiting Stereo Depth Estimation From a Sequence-to-Sequence Perspective with Transformers, arXiv 2020, depth estimation. Zhaoshuo Li, Xingtong Liu ...
Jul 19, 2022 · In this work, for the first time we explore the global context learning potentials of ViTs for dense visual prediction (eg, semantic segmentation).
This repo is used for recording, tracking, and benchmarking several recent transformer-based visual segmentation methods, as a supplement to our survey.
In this paper, we present a general end-to-end approach to sequence learning that makes minimal assumptions on the sequence structure. Our method uses a ...
In this paper, we aim to provide an alternative perspective by treating semantic segmenta- tion as a sequence-to-sequence prediction task. Specifically, we ...
Mar 15, 2023 · The Transformer architecture has taken deep learning by storm. Initially designed for solving sequence-to-sequence tasks such as machine translation, the ...
A transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, ...