Fuse Local and Global Semantics in Representation Learning
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Abstract (arXiv)
We propose Fuse Local and Global Semantics in Representation Learning (FLAGS) to generate richer representations. FLAGS aims at extract both global and local semantics from images to benefit various downstream tasks. It shows promising results under common linear evaluation protocol. We also conduct detection and segmentation on PASCAL VOC and COCO to show the representations extracted by FLAGS are transferable.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 21 of its 35 references that are in the map. First posted on arXiv 28 Feb 2022.
Builds on
21 of 35 references.
References · 21
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
- 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
- 2020A Simple Framework for Contrastive Learning of Visual Representationscited by 11,979
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
- 2019Momentum Contrast for Unsupervised Visual Representation Learningcited by 7,482
- 2020End-to-End Object Detection with Transformerscited by 7,384
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,415
- 2020Improved Baselines with Momentum Contrastive Learningcited by 2,282
- 2018Unsupervised Representation Learning by Predicting Image Rotationscited by 1,586
- 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,430
- 2016Context Encoders: Feature Learning by Inpaintingcited by 1,424
9 more
- 2016Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzlescited by 1,379
- 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
- 2016Colorful Image Colorizationcited by 1,113
- 2017Revisiting Unreasonable Effectiveness of Data in Deep Learning Eracited by 916
- 2020Dense Contrastive Learning for Self-Supervised Visual Pre-Trainingcited by 512
- 2020What Should Not Be Contrastive in Contrastive Learningcited by 224
- 2020Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biasescited by 143
- 2018Learning Image Representations by Completing Damaged Jigsaw Puzzlescited by 66
- 2020Can Semantic Labels Assist Self-Supervised Visual Representation Learning?cited by 16
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