uniformer: unified transformer for efficient spatiotemporal representation learning

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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. Ultimate-Awesome-Transformer-Attention This repo contains a comprehensive paper list of Vision Transformer & Attention, including papers, codes, and related websites. 29: . 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. 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. 20. i10-index. 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 (b) Timesformer. TransformerUniFormerTransformer 3D . We adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation. ( Actively keep updating) If you find some ignored papers, feel free to create pull requests, open issues, or email me. 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. Spatio-temporal representational learning has been widely adopted in various fields such as action recognition, video object . 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 . 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. The overall framework is presented in Fig. 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). 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. Verified email at siat.ac.cn. Yali Wang. For. UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning . Deep Learning Computer Vision Pattern Recognition. 30. 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. The recent advances in this research have been mainly driven by 3D convolutional neural networks and vision transformers. 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. 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. 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. Abstract: Learning discriminative spatiotemporal representation is the key problem of video understanding. To our knowledge, this work is the first to improve the transformer with spatiotemporal information in RL. !O(_)O . 32. On the benefits of maximum likelihood estimation for Regression and Forecasting. It currently includes code and models for the following tasks: Image Classification; Video Classification 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 . 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. iclr2022uniformer: unified transformer for efficient spatiotemporal representation learning Love 2022-02-26 15:55:49 490 2 Transformer transformer 2.We first define the notations in Sec. The recent advances in this research have been mainly . We adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation. Without any extra training data, UniFormer ( Uni fied trans Former) is introduce in arxiv, which effectively unifies 3D convolution and spatiotemporal self-attention in a concise transformer format. Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning. Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences. 3.1, then describe VPT formally in Sec. View Hongsheng Li's profile, machine learning models, research papers, and code. 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. UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning . Transformer2.1 Transformer2.2 encoder2.3 decoder Datawhale9 . 1 Highly Influenced PDF View 7 excerpts, cites methods and results Different from traditional UNIFORMER: UNIFIED TRANSFORMER FOR EFFICIENT SPATIOTEMPORAL REPRESENTATION LEARNING COSFORMER : RETHINKING SOFTMAX IN ATTENTION ! UniFormer. layer. 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. 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. Essentially, researchers are confronted with two separate issues in visual data, such as photographs and movies. For visual recognition, representation learning is a crucial research area. 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. 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".. UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning. Learning to Remember Patterns: Pattern Matching Memory Networks for Traffic Forecasting. PDF Abstract Code Edit (a) DeiT. 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. DJ Zhang, K Li, Y Wang, Y Chen, S Chandra, Y Qiao, L Liu, MZ Shou . ICLR2022, 2022. The analysis of long sequence data remains challenging in many real-world applications. Original Transformer-based models. 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. 2022) Paper:. It adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation. 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". K Li, Y Wang, P Gao, G Song, Y Liu, H Li, Y Qiao. As two showcases, we. We propose a novel architecture, ChunkFormer, that improves the existing Transformer framework to handle the challenges while dealing with long time series. 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 Yu Qiao . 2021 A Simple Long-Tailed Recognition Baseline via Vision-Language Model . 20. Uniformer: Unified Transformer for Efficient Spatiotemporal Representation Learning (, Chinese Academy of Sciences, Ja. 3.2. UniFormer. Kunchang Li, Yali Wang, Peng Gao, Guanglu Song, Yu Liu, Hongsheng Li, Yu Qiao, ICLR 2022 / Paper / Code. Yu Qiao . Transformer. This list is maintained by Min-Hung Chen. Inefficient computation is frequently . We adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation. 2: Visualization of vision transformers. 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. It currently includes code and models for the following tasks: The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format. 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. csdnaaai2020aaai2020aaai2020aaai2020 . 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. See more researchers and engineers like Hongsheng Li. Fig. An Efficient MLP-Like Backbone for Spatial-Temporal Representation Learning. 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. 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. 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 .
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