Summary of related papers on visual attention

Overview

This repo is built for paper: Attention Mechanisms in Computer Vision: A Survey paper

image

πŸ”₯ (citations > 200)

  • TODO : Code about different attention mechanisms will come soon.
  • TODO : Code link will come soon.
  • TODO : collect more related papers. Contributions are welcome.

Channel attention

  • Squeeze-and-Excitation Networks(CVPR2018) pdf, (PAMI2019 version) pdf πŸ”₯
  • Image superresolution using very deep residual channel attention networks(ECCV2018) pdf πŸ”₯
  • Context encoding for semantic segmentation(CVPR2018) pdf πŸ”₯
  • Spatio-temporal channel correlation networks for action classification(ECCV2018) pdf
  • Global second-order pooling convolutional networks(CVPR2019) pdf
  • Srm : A style-based recalibration module for convolutional neural networks(ICCV2019) pdf
  • You look twice: Gaternet for dynamic filter selection in cnns(CVPR2019) pdf
  • Second-order attention network for single image super-resolution(CVPR2019) pdf πŸ”₯
  • Spsequencenet: Semantic segmentation network on 4d point clouds(CVPR2020) pdf
  • Ecanet: Efficient channel attention for deep convolutional neural networks (CVPR2020) pdf πŸ”₯
  • Gated channel transformation for visual recognition(CVPR2020) pdf
  • Fcanet: Frequency channel attention networks(ICCV2021) pdf

Spatial attention

  • Recurrent models of visual attention(NeurIPS2014), pdf πŸ”₯
  • Show, attend and tell: Neural image caption generation with visual attention(PMLR2015) pdf πŸ”₯
  • Draw: A recurrent neural network for image generation(ICML2015) pdf πŸ”₯
  • Spatial transformer networks(NeurIPS2015) pdf πŸ”₯
  • Multiple object recognition with visual attention(ICLR2015) pdf πŸ”₯
  • Action recognition using visual attention(arXiv2015) pdf πŸ”₯
  • Videolstm convolves, attends and flows for action recognition(arXiv2016) pdf πŸ”₯
  • Look closer to see better: Recurrent attention convolutional neural network for fine-grained image recognition(CVPR2017) pdf πŸ”₯
  • Learning multi-attention convolutional neural network for fine-grained image recognition(ICCV2017) pdf πŸ”₯
  • Diversified visual attention networks for fine-grained object classification(TMM2017) pdf πŸ”₯
  • Attentional pooling for action recognition(NeurIPS2017) pdf πŸ”₯
  • Non-local neural networks(CVPR2018) pdf πŸ”₯
  • Attentional shapecontextnet for point cloud recognition(CVPR2018) pdf
  • Relation networks for object detection(CVPR2018) pdf πŸ”₯
  • a2-nets: Double attention networks(NeurIPS2018) pdf πŸ”₯
  • Attention-aware compositional network for person re-identification(CVPR2018) pdf πŸ”₯
  • Tell me where to look: Guided attention inference network(CVPR2018) pdf πŸ”₯
  • Pedestrian alignment network for large-scale person re-identification(TCSVT2018) pdf πŸ”₯
  • Learn to pay attention(ICLR2018) pdf πŸ”₯
  • Attention U-Net: Learning Where to Look for the Pancreas(MIDL2018) pdf πŸ”₯
  • Psanet: Point-wise spatial attention network for scene parsing(ECCV2018) pdf πŸ”₯
  • Self attention generative adversarial networks(ICML2019) pdf πŸ”₯
  • Attentional pointnet for 3d-object detection in point clouds(CVPRW2019) pdf
  • Co-occurrent features in semantic segmentation(CVPR2019) pdf
  • Attention augmented convolutional networks(ICCV2019) pdf πŸ”₯
  • Local relation networks for image recognition(ICCV2019) pdf
  • Latentgnn: Learning efficient nonlocal relations for visual recognition(ICML2019) pdf
  • Graph-based global reasoning networks(CVPR2019) pdf πŸ”₯
  • Gcnet: Non-local networks meet squeeze-excitation networks and beyond(ICCVW2019) pdf πŸ”₯
  • Asymmetric non-local neural networks for semantic segmentation(ICCV2019) pdf πŸ”₯
  • Looking for the devil in the details: Learning trilinear attention sampling network for fine-grained image recognition(CVPR2019) pdf
  • Second-order non-local attention networks for person re-identification(ICCV2019) pdf πŸ”₯
  • End-to-end comparative attention networks for person re-identification(ICCV2019) pdf πŸ”₯
  • Modeling point clouds with self-attention and gumbel subset sampling(CVPR2019) pdf
  • Diagnose like a radiologist: Attention guided convolutional neural network for thorax disease classification(arXiv 2019) pdf
  • L2g autoencoder: Understanding point clouds by local-to-global reconstruction with hierarchical self-attention(arXiv 2019) pdf
  • Generative pretraining from pixels(PMLR2020) pdf
  • Exploring self-attention for image recognition(CVPR2020) pdf
  • Cf-sis: Semantic-instance segmentation of 3d point clouds by context fusion with self attention(MM20) pdf
  • Disentangled non-local neural networks(ECCV2020) pdf
  • Relation-aware global attention for person re-identification(CVPR2020) pdf
  • Segmentation transformer: Object-contextual representations for semantic segmentation(ECCV2020) pdf πŸ”₯
  • Spatial pyramid based graph reasoning for semantic segmentation(CVPR2020) pdf
  • Self-supervised Equivariant Attention Mechanism for Weakly Supervised Semantic Segmentation(CVPR2020) pdf
  • End-to-end object detection with transformers(ECCV2020) pdf πŸ”₯
  • Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling(CVPR2020) pdf
  • Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers(CVPR2021) pdf
  • An image is worth 16x16 words: Transformers for image recognition at scale(ICLR2021) pdf πŸ”₯
  • An empirical study of training selfsupervised vision transformers(CVPR2021) pdf
  • Ocnet: Object context network for scene parsing(IJCV 2021) pdf πŸ”₯
  • Point transformer(ICCV 2021) pdf
  • PCT: Point Cloud Transformer (CVMJ 2021) pdf
  • Pre-trained image processing transformer(CVPR 2021) pdf
  • An empirical study of training self-supervised vision transformers(ICCV 2021) pdf
  • Segformer: Simple and efficient design for semantic segmentation with transformers(arxiv 2021) pdf
  • Beit: Bert pre-training of image transformers(arxiv 2021) pdf
  • Beyond selfattention: External attention using two linear layers for visual tasks(arxiv 2021) pdf
  • Query2label: A simple transformer way to multi-label classification(arxiv 2021) pdf
  • Transformer in transformer(arxiv 2021) pdf

Temporal attention

  • Jointly attentive spatial-temporal pooling networks for video-based person re-identification (ICCV 2017) pdf πŸ”₯
  • Video person reidentification with competitive snippet-similarity aggregation and co-attentive snippet embedding(CVPR 2018) pdf
  • Scan: Self-and-collaborative attention network for video person re-identification (TIP 2019) pdf

Branch attention

  • Training very deep networks, (NeurIPS 2015) pdf πŸ”₯
  • Selective kernel networks,(CVPR 2019) pdf πŸ”₯
  • CondConv: Conditionally Parameterized Convolutions for Efficient Inference (NeurIPS 2019) pdf
  • Dynamic convolution: Attention over convolution kernels (CVPR 2020) pdf
  • ResNest: Split-attention networks (arXiv 2020) pdf πŸ”₯

ChannelSpatial attention

  • Residual attention network for image classification (CVPR 2017) pdf πŸ”₯
  • SCA-CNN: spatial and channel-wise attention in convolutional networks for image captioning,(CVPR 2017) pdf πŸ”₯
  • CBAM: convolutional block attention module, (ECCV 2018) pdf πŸ”₯
  • Harmonious attention network for person re-identification (CVPR 2018) pdf πŸ”₯
  • Recalibrating fully convolutional networks with spatial and channel β€œsqueeze and excitation” blocks (TMI 2018) pdf
  • Mancs: A multi-task attentional network with curriculum sampling for person re-identification (ECCV 2018) pdf πŸ”₯
  • Bam: Bottleneck attention module(BMVC 2018) pdf πŸ”₯
  • Pvnet: A joint convolutional network of point cloud and multi-view for 3d shape recognition (ACM MM 2018) pdf
  • Learning what and where to attend,(ICLR 2019) pdf
  • Dual attention network for scene segmentation (CVPR 2019) pdf πŸ”₯
  • Abd-net: Attentive but diverse person re-identification (ICCV 2019) pdf
  • Mixed high-order attention network for person re-identification (ICCV 2019) pdf
  • Mlcvnet: Multi-level context votenet for 3d object detection (CVPR 2020) pdf
  • Improving convolutional networks with self-calibrated convolutions (CVPR 2020) pdf
  • Relation-aware global attention for person re-identification (CVPR 2020) pdf
  • Strip Pooling: Rethinking spatial pooling for scene parsing (CVPR 2020) pdf
  • Rotate to attend: Convolutional triplet attention module, (WACV 2021) pdf
  • Coordinate attention for efficient mobile network design (CVPR 2021) pdf
  • Simam: A simple, parameter-free attention module for convolutional neural networks (ICML 2021) pdf

SpatialTemporal attention

  • An end-to-end spatio-temporal attention model for human action recognition from skeleton data(AAAI 2017) pdf πŸ”₯
  • Diversity regularized spatiotemporal attention for video-based person re-identification (ArXiv 2018) πŸ”₯
  • Interpretable spatio-temporal attention for video action recognition (ICCVW 2019) pdf
  • Hierarchical lstms with adaptive attention for visual captioning, (TPAMI 2020) pdf
  • Stat: Spatial-temporal attention mechanism for video captioning, (TMM 2020) pdf_link
  • Gta: Global temporal attention for video action understanding (ArXiv 2020) pdf
  • Multi-granularity reference-aided attentive feature aggregation for video-based person re-identification (CVPR 2020) pdf
  • Read: Reciprocal attention discriminator for image-to-video re-identification, (ECCV 2020) pdf
  • Decoupled spatial-temporal transformer for video inpainting (ArXiv 2021) pdf
Comments
  • Implementation

    Implementation

    Congratulations on publishing such a comprehensive paper.

    Would be nice to implement some of the methods mentioned therein and package that as a library with a focus on readability.

    opened by sayakpaul 3
  • Consideration of

    Consideration of "Local Importance-based Pooling" (LIP) as an attention method

    Hi Menghao,

    Thanks for your great and helpful work. I regard our proposed LIP [1] also as a local attention method designed for the spatial downsampling procedure, whose weights are produced by a lightweight sub-network. Though being designed for spatial downsizing, it still falls in the regime of attention approaches. Could you please consider LIP also as an attention method especially for the pooling procedure?

    By the way, the work TAM [2] done by our group is accepted by ICCV 2021. Please consider updating the venue of this paper.

    [1] LIP: Local Importance-Based Pooling. ICCV 2019. [2] TAM: Temporal Adaptive Module for Video Recognition. ICCV 2021.

    opened by sebgao 1
  • Source code

    Source code

    Hello, author, I'm a student. I want to use your code for research, but the code in the code folder I downloaded is empty. Can you share with me? My email: [email protected]

    opened by Burgess-00 1
  • The top date is wrong I guess?

    The top date is wrong I guess?

    It seem that the top date comes from a pre-filled template and is set to:

    JOURNAL OF LATEX CLASS FILES, VOL. 14, NO. 8, AUGUST 2015 1

    I guess it is wrong given how recent the paper is.

    opened by yassineAlouini 1
  • Reference needed for ocr.py

    Reference needed for ocr.py

    @MenghaoGuo @idansc @Gsunshine @PengtaoJiang @uyzhang Can you please provide paper refence for code /spatial_attentions/ocr.py

    I guess it is for text recognition with attention but I could not gate any relevant paper in provided paper list.

    I want to train the network and check results

    opened by AniketGurav 2
Owner
MenghaoGuo
Second-year Ph.D candidate at G2 group, Tsinghua University.
MenghaoGuo
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