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C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection

https://doi.org/10.1109/cvpr52688.2022.00685

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning

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Exploring the Limits of Weakly Supervised Pretraining

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Large Batch Optimization for Object Detection: Training COCO in 12 minutes

inferredcites →https://doi.org/10.1007/978-3-319-10602-1_48openalex-snapshot

Microsoft COCO: Common Objects in Context

inferredcites →https://doi.org/10.1007/s11263-009-0275-4openalex-snapshot

The Pascal Visual Object Classes (VOC) Challenge

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ImageNet Large Scale Visual Recognition Challenge

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CornerNet: Detecting Objects as Paired Keypoints

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Progressive rectification network for irregular text recognition

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The Pareto, Zipf and other power laws

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Deep Residual Learning for Image Recognition

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You Only Look Once: Unified, Real-Time Object Detection

inferredcites →https://doi.org/10.1109/cvpr.2017.106openalex-snapshot

Feature Pyramid Networks for Object Detection

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SphereFace: Deep Hypersphere Embedding for Face Recognition

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Single-Shot Refinement Neural Network for Object Detection

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CosFace: Large Margin Cosine Loss for Deep Face Recognition

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Cascade R-CNN: Delving Into High Quality Object Detection

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ArcFace: Additive Angular Margin Loss for Deep Face Recognition

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LVIS: A Dataset for Large Vocabulary Instance Segmentation

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Class-Balanced Loss Based on Effective Number of Samples

inferredcites →https://doi.org/10.1109/cvpr41558.2019openalex-snapshot

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01100openalex-snapshot

Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax

inferredcites →https://doi.org/10.1109/cvpr42600.2020.01168openalex-snapshot

Equalization Loss for Long-Tailed Object Recognition

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00173openalex-snapshot

Equalization Loss v2: A New Gradient Balance Approach for Long-tailed Object Detection

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00239openalex-snapshot

Distribution Alignment: A Unified Framework for Long-tail Visual Recognition

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00312openalex-snapshot

Adaptive Class Suppression Loss for Long-Tail Object Detection

inferredcites →https://doi.org/10.1109/cvpr46437.2021.00957openalex-snapshot

Seesaw Loss for Long-Tailed Instance Segmentation

inferredcites →https://doi.org/10.1109/iccv.2015.169openalex-snapshot

Fast R-CNN

inferredcites →https://doi.org/10.1109/iccv.2017.322openalex-snapshot

Mask R-CNN

inferredcites →https://doi.org/10.1109/iccv48922.2021.00340openalex-snapshot

Exploring Classification Equilibrium in Long-Tailed Object Detection

inferredcites →https://doi.org/10.1109/iccv48922.2021.00344openalex-snapshot

FASA: Feature Augmentation and Sampling Adaptation for Long-Tailed Instance Segmentation

inferredcites →https://doi.org/10.1109/tpami.2016.2577031openalex-snapshot

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

inferredcites →https://doi.org/10.1109/tpami.2018.2858826openalex-snapshot

Focal Loss for Dense Object Detection

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Proceedings of the 28th ACM International Conference on Multimedia

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Forest R-CNN: Large-Vocabulary Long-Tailed Object Detection and Instance Segmentation

inferredcites →https://doi.org/10.29007/3b2lopenalex-snapshot

Efficient Cipher-Only Attack of a Stream Cipher in RDHEI Based on Pixel Smoothness Estimation

inferredcites →https://doi.org/10.48550/arxiv.1704.06369openalex-snapshot

NormFace: L2 Hypersphere Embedding for Face Verification

inferredcites →https://doi.org/10.48550/arxiv.1909.02466openalex-snapshot

FreeAnchor: Learning to Match Anchors for Visual Object Detection

inferredcites →https://doi.org/10.48550/arxiv.2007.10740openalex-snapshot

Balanced Meta-Softmax for Long-Tailed Visual Recognition

inferredcites →https://doi.org/10.7551/mitpress/1120.001.0001openalex-snapshot

Advances in Neural Information Processing Systems 14

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Dynamic dual mining framework for long-tailed out-of-distribution detection

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Balanced Loss Function for Long-tailed Semi-supervised Ship Detection

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Logit Normalization for Long-Tail Object Detection

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Deep Learning-Based Computer Vision Methods for Complex Traffic Environments Perception: A Review

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Underwater object detection in noisy imbalanced datasets

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Gradient-based Sampling for Class Imbalanced Semi-supervised Object Detection

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Reconciling Object-Level and Global-Level Objectives for Long-Tail Detection

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Two-Stage Few-Shot Contrastive Enhancement for Long-Tailed Traffic Participant Detection

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DuMo: A Dual-Model Framework for Effective Long-tailed Object Detection

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Image-Based PM2.5 Estimation From Imbalanced Data Distribution Using Prior-Enhanced Neural Networks

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Align and Complete Samples in Remote Sensing Fine-Grained Rigid Object Detection

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DECOR+: Energy-Balanced Representation Enhancement for Long-Tailed Remote Sensing Scene Recognition

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Federated Learning With Long-Tailed Data via Representation Unification and Classifier Rectification

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A Systematic Review on Long-Tailed Learning

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.