Data-Efficient Challenges in Visual Inductive Priors: A Retrospective | Paper Citation Record

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This paper cites https://cocodataset.org/.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective https://cocodataset.org/

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This paper cites Accessed: 2024-07-02.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Accessed: 2024-07-02

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This paper cites https://github.com/ultralytics/ultralytics.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective https://github.com/ultralytics/ultralytics

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Kaggle.com: Tiny imagenet

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Deep learning on small datasets without pre-training using cosine loss

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Revisiting resnets: Improved training and scaling strategies

Reference 6

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This paper cites Is Space-Time Attention All You Need for Video Understanding?.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Is Space-Time Attention All You Need for Video Understanding?

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Yolov4: Optimal speed and accuracy of object detection, 2020

Reference 8

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Soft-nms--improving object detection with one line of code

Reference 9

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This paper cites Tune it or don't use it: Benchmarking data-efficient image classification.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Tune it or don't use it: Benchmarking data-efficient image classification

Reference 10

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This paper cites VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective VIPriors 1: Visual Inductive Priors for Data-Efficient Deep Learning Challenges

Reference 11

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This paper cites Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Iglovikov, Eugene Khvedchenya, Alex Parinov, Mikhail Druzhinin, and Alexandr A

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Cascade r-cnn: Delving into high quality object detection

Reference 14

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Cascade r-cnn: Delving into high quality object detection

Reference 15

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Gcnet: Non-local networks meet squeeze-excitation networks and beyond

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective End-to-end object detection with transformers

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Carreira and A

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This paper cites 2nd place scheme on action recognition track of eccv 2020 vipriors challenges: An efficient optical flow stream guided framework.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective 2nd place scheme on action recognition track of eccv 2020 vipriors challenges: An efficient optical flow stream guided framework

Reference 19

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Hybrid task cascade for instance segmentation

Reference 20

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Hybrid task cascade for instance segmentation

Reference 21

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Gridmask data augmentation, 2020 b

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Exploring simple siamese representation learning

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Improved Baselines with Momentum Contrastive Learning

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective An empirical study of training self-supervised vision transformers

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Stitcher: Feedback-driven data provider for object detection, 2020 d

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Dual path networks

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Vision Transformer Adapter for Dense Predictions

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Masked-attention mask transformer for universal image segmentation

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Sparse instance activation for real-time instance segmentation

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Kim, and Jaegul Choo

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Total recall: Automatic query expansion with a generative feature model for object retrieval

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective The cityscapes dataset for semantic urban scene understanding

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective AutoAugment: Learning Augmentation Policies from Data

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Randaugment: Practical automated data augmentation with a reduced search space

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Randaugment: Practical automated data augmentation with a reduced search space

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Deformable convolutional networks

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective TCLR: Temporal Contrastive Learning for Video Representation

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Imagenet: A large-scale hierarchical image database

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Arcface: Additive angular margin loss for deep face recognition

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Improved Regularization of Convolutional Neural Networks with Cutout

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Multiscale vision transformers

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Instaboost: Boosting instance segmentation via probability map guided copy-pasting

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Feichtenhofer , H

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Fernando , E

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Res2net: A new multi-scale backbone architecture

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective YOLOX: Exceeding YOLO Series in 2021

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Cubuk, Quoc V

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Rich feature hierarchies for accurate object detection and semantic segmentation

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective 2nd place solution to eccv 2020 vipriors object detection challenge, 2020

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Rethinking channel dimensions for efficient model design

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Towards good practice for action recognition with spatiotemporal 3d convolutions

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Deep residual learning for image recognition, 2015

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Deep residual learning for image recognition

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Mask r-cnn

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Rethinking imagenet pre-training

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Momentum contrast for unsupervised visual representation learning

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Momentum contrast for unsupervised visual representation learning

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Bag of tricks for image classification with convolutional neural networks, 2018

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Cubuk, Barret Zoph, Justin Gilmer, and Balaji Lakshminarayanan

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective In Defense of the Triplet Loss for Person Re-Identification

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Learning curves for analysis of deep networks

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Observation 21a6f385-bfe1-4e8d-abfd-e502cd617968 · outbound

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Sspnet: Scale selection pyramid network for tiny person detection from uav images

Reference 64

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Observation fe088dee-9142-43da-be0c-4f2abe6eb1ca · outbound

This paper cites Searching for mobilenetv3.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Searching for mobilenetv3

Reference 65

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Observation 5f2d13db-75fc-4dc3-9bf1-e760fec5795d · outbound

This paper cites Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge

Reference 66

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Observation 87d62803-9256-496f-91e0-90df8fb88128 · outbound

This paper cites Squeeze-and-excitation networks.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Squeeze-and-excitation networks

Reference 67

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Observation 18718bc0-e14c-4a33-b219-57ed1675bcea · outbound

This paper cites Deep networks with stochastic depth.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Deep networks with stochastic depth

Reference 68

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Observation 04c04d1f-2576-4f1b-8939-f03ca0c4178c · outbound

This paper cites Mask scoring r-cnn.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Mask scoring r-cnn

Reference 69

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This paper cites What makes ImageNet good for transfer learning?.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective What makes ImageNet good for transfer learning?

Reference 70

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Observation f460e3ad-5b58-431b-9cee-c30216e327c0 · outbound

This paper cites “kallis” crcv vipriors challenge submission.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective “kallis” crcv vipriors challenge submission

Reference 71

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Averaging Weights Leads to Wider Optima and Better Generalization

Reference 73

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Observation 3c0cda33-b657-4ba6-9b1d-8501891fa370 · outbound

This paper cites Semask: Semantically masked transformers for semantic segmentation.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Semask: Semantically masked transformers for semantic segmentation

Reference 74

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Unresolved cited work

Reference 75

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Unresolved cited work

Reference 76

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective The Kinetics Human Action Video Dataset

Reference 77

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Observation a5c8e57d-1be3-4e10-99c7-0d830e72791f · outbound

This paper cites Hallucination In Object Detection -- A Study In Visual Part Verification.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Hallucination In Object Detection -- A Study In Visual Part Verification

Reference 78

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Observation 1fdac787-1764-4655-8fe2-2bbd51313873 · outbound

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Mask transfiner for high-quality instance segmentation

Reference 79

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Data-efficient deep learning method for image classification using data augmentation, focal cosine loss, and ensemble, 2020 a

Reference 80

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Learning temporally invariant and localizable features via data augmentation for video recognition

Reference 81

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Augmentation for small object detection

Reference 82

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Observation 89ff1bb4-1797-4f2b-9c25-7d8ed4163c50 · outbound

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Everything you need to know about few-shot learning

Reference 83

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Selective kernel networks

Reference 84

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Observation 4f42d571-79eb-430b-af4a-49f1ed1bd069 · outbound

This paper cites CBNet: A Composite Backbone Network Architecture for Object Detection.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective CBNet: A Composite Backbone Network Architecture for Object Detection

Reference 86

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Unresolved cited work

Reference 87

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Feature pyramid networks for object detection, 2017 a

Reference 88

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Observation ced18643-f372-43c4-a885-7c9af9489ccd · outbound

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Focal loss for dense object detection

Reference 89

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Observation c78dde3c-8db9-4028-9af1-2d3f7168c5e9 · outbound

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Diversification is all you need: Towards data efficient image understanding

Reference 90

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Multi-scale methods

Reference 91

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Cbnet: A novel composite backbone network architecture for object detection

Reference 92

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Swin transformer: Hierarchical vision transformer using shifted windows

Reference 93

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Observation de12811d-cc0d-4bcc-a084-55f0f3072b63 · outbound

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Video Swin Transformer

Reference 94

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective TANet: Robust 3D Object Detection from Point Clouds with Triple Attention

Reference 95

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Observation 97d4c9ce-7c7a-4846-87ac-50c8365b29d9 · outbound

This paper cites A convnet for the 2020s.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective A convnet for the 2020s

Reference 96

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Observation eb45ca3e-f485-41d3-908d-c0ddfea0b991 · outbound

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective AutoMix: Unveiling the Power of Mixup for Stronger Classifiers

Reference 97

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Observation 82afd940-e029-4d20-a839-ec7b14ddaed9 · outbound

This paper cites Vipriors object detection challenge, 2020.

Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Vipriors object detection challenge, 2020

Reference 98

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective A technical report for vipriors image classification challenge, 2020

Reference 99

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Curvature-balanced feature manifold learning for long-tailed classification

Reference 100

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Test-time augmentation for deep learning-based cell segmentation on microscopy images

Reference 101

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective When Does Label Smoothing Help?

Reference 102

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Observation c9f3c021-349d-4eb6-963d-5e81e7bb8729 · outbound

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Semi-supervised transformer with fpn for bikes parts detection

Reference 103

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Observation aa6ed07d-3b00-4c53-9d06-f4537cf315f5 · outbound

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Data-Efficient Challenges in Visual Inductive Priors: A Retrospective Libra r-cnn: Towards balanced learning for object detection, 2019

Reference 104

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