uniformer: unified transformer for efficient spatiotemporal representation learning

With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1 accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than other state-of-the-art methods. With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1 accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than other state-of-the-art methods. UniFormer ( Uni fied trans Former) is introduce in arxiv (more details can be found in arxiv ), which can seamlessly integrate merits of convolution and self-attention in a concise transformer format. The overall framework is presented in Fig. ICLR2022, 2022. It currently includes code and models for the following tasks: K Li, Y Wang, P Gao, G Song, Y Liu, H Li, Y Qiao. UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning . UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning . Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning - NewsBreak It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. iclr2022uniformer: unified transformer for efficient spatiotemporal representation learning Love 2022-02-26 15:55:49 490 2 Transformer transformer Different from the typical transformer blocks, the relation aggregators in our UniFormer block are equipped with local and global token affinity respectively in shallow and deep layers, allowing to tackle both redundancy and dependency for efficient and effective representation learning. This repo is the official implementation of "UniFormer: Unifying Convolution and Self-attention for Visual Recognition" and "UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning". 3.1, then describe VPT formally in Sec. Without any extra training data, 30. 2022) Paper:. 3.2. On the one hand, there is a great deal of local redundancy; for example, visual material in a particular region (space, time, or space-time) is often comparable. UniFormer ( Uni fied trans Former) is introduce in arxiv, which effectively unifies 3D convolution and spatiotemporal self-attention in a concise transformer format. 2021 A Simple Long-Tailed Recognition Baseline via Vision-Language Model . We adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation. Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences. It was introduced in the paper UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning by Li et al, and first released in this repository. 2: Visualization of vision transformers. Model description The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format. Different from traditional Inefficient computation is frequently . For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60.9% and 71.2% top-1 accuracy respectively. To our knowledge, this work is the first to improve the transformer with spatiotemporal information in RL. layer. It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. TL;DR: We propose UniFormerV2, which aims to arm the well-pretrained vision transformer with efficient video UniFormer designs, and achieves state-of-the-art results on 8 popular video benchmarks. Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning (, Chinese Academy of Sciences, Ja. We take the well-known Vision Transformers (ViTs) in both image and video domains (i.e., DeiT [] and TimeSformer []) for illustration, where we respectively show the feature maps, spatial and temporal attention maps from the 3rd layer of these ViTs.We find that, such ViTs learns local representations with redundant global . 32. We adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation. With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1 accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than other state-of-the-art methods. TransformerUniFormerTransformer 3D . 20. i10-index. We propose Visual-Prompt Tuning (VPT) for adapting large pre-trained vision Transformer models.VPT injects a small number of learnable parameters into Transformer's input space and keeps the backbone frozen during the downstream training stage. UniFormer. Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting. For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60.9% and 71.2% top-1 accuracy respectively. This novel interpretation enables us to better understand the connections between GCNs (GCN, GAT) and CNNs and further inspires us to design more Unified GCNs (UGCNs). PDF Abstract Code Edit (a) DeiT. Yu Qiao . ( Actively keep updating) If you find some ignored papers, feel free to create pull requests, open issues, or email me. UNIFORMER: UNIFIED TRANSFORMER FOR EFFICIENT SPATIOTEMPORAL REPRESENTATION LEARNING COSFORMER : RETHINKING SOFTMAX IN ATTENTION ! See more researchers and engineers like Hongsheng Li. (b) Timesformer. Deep Learning Computer Vision Pattern Recognition. Abstract: Learning discriminative spatiotemporal representation is the key problem of video understanding. It adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation. UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning. 1 Highly Influenced PDF View 7 excerpts, cites methods and results Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning. Transformer. UniFormer ( Uni fied trans Former) is introduce in arxiv (more details can be found in arxiv ), which can seamlessly integrate merits of convolution and self-attention in a concise transformer format. The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format. Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning K Li, Y Wang, P Gao, G Song, Y Liu, H Li, Y Qiao arXiv preprint arXiv:2201.04676 , 2022 UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning Kunchang Li , Yali Wang , Peng Gao , Guanglu Song , Yu Liu , Hongsheng Li , Yu Qiao View Code API Access Call/Text an Expert * Published as a conference paper at ICLR 2022; 19pages, 7 figures Access Paper or Ask Questions Essentially, researchers are confronted with two separate issues in visual data, such as photographs and movies. An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning. It currently includes code and models for the following tasks: Image Classification; Video Classification We adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation. On the benefits of maximum likelihood estimation for Regression and Forecasting. Yu Qiao . !O(_)O . This list is maintained by Min-Hung Chen. Original Transformer-based models. Spatio-temporal representational learning has been widely adopted in various fields such as action recognition, video object . The analysis of long sequence data remains challenging in many real-world applications. For visual recognition, representation learning is a crucial research area. With only ImageNet-1K pretraining, our UniFormer achieves 82.9%/84.8% top-1 accuracy on Kinetics-400/Kinetics-600, while requiring 10x fewer GFLOPs than other state-of-the-art methods. Kunchang Li, Yali Wang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, Yu Qiao, ICLR 2022 / Paper / Code. We propose a novel architecture, ChunkFormer, that improves the existing Transformer framework to handle the challenges while dealing with long time series. For. Yali Wang. The recent advances in this research have been mainly . View Hongsheng Li's profile, machine learning models, research papers, and code. Based on these observations, we propose a novel Unified transFormer (UniFormer) which seamlessly integrates merits of 3D convolution and spatiotemporal self-attention in a concise transformer format, and achieves a preferable balance between computation and accuracy. 29: . The recent advances in this research have been mainly driven by 3D convolutional neural networks and vision transformers. A shifted chunk Transformer with pure self-attention blocks that can learn hierarchical spatio-temporal features from a local tiny patch to a global video clip and outperforms previous state-of-the-art approaches onKinetics-400, Kinetics-600, UCF101, and HMDB51. Verified email at siat.ac.cn. A novel and general-purpose Inception Transformer is presented that effectively learns comprehensive features with both high- and low-frequency information in visual data and achieves impressive performance on image classication, COCO detection and ADE20K segmentation. This repo is the official implementation of "UniFormer: Unifying Convolution and Self-attention for Visual Recognition" and "UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning".. As two showcases, we. For Something-Something V1 and V2, our UniFormer achieves new state-of-the-art performances of 60.8% and 71.4% top-1 accuracy respectively. It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global . 2.We first define the notations in Sec. In each attention block, we sequentially execute attention computation twice: the first to process the temporal sequence of the input and the latter to manage the spatial state. Back2Future: Leveraging Backfill Dynamics for Improving Real-time Predictions in Future. Ultimate-Awesome-Transformer-Attention This repo contains a comprehensive paper list of Vision Transformer & Attention, including papers, codes, and related websites. 20. It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global . Transformer2.1 Transformer2.2 encoder2.3 decoder Datawhale9 . Fig. csdnaaai2020aaai2020aaai2020aaai2020 . UniFormer. Abstract: It is a challenging task to learn rich and multi-scale spatiotemporal semantics from high-dimensional videos, due to large local redundancy and complex global dependency between video frames. DJ Zhang, K Li, Y Wang, Y Chen, S Chandra, Y Qiao, L Liu, MZ Shou .

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