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Awesome-LongTailed-Learning
A codebase and a curated list of awesome deep long-tailed learning (TPAMI 2023).
https://github.com/Vanint/Awesome-LongTailed-Learning
Last synced: 2 days ago
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2. Top-tier Conference Papers
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2021
- Self supervision to distillation for long-tailed visual recognition - NJU/SSD-LT) |
- Parametric contrastive learning - research/Parametric-Contrastive-Learning) |
- Learning of visual relations: The devil is in the tails
- VideoLT: Large-scale long-tailed video recognition
- ACE: Ally complementary experts for solving long-tailed recognition in one-shot
- Self-Damaging Contrastive Learning - Group/SDCLR) |
- Delving into deep imbalanced regression - regression) |
- Improving contrastive learning on imbalanced seed data via open-world sampling - Group/MAK) |
- Semi-supervised semantic segmentation via adaptive equalization learning - AEL) |
- On model calibration for long-tailed object detection and instance segmentation
- Label-imbalanced and group-sensitive classification under overparameterization
- Towards calibrated model for long-tailed visual recognition from prior perspective - LT) |
- Supercharging imbalanced data learning with energy-based contrastive representation transfer
- VideoLT: Large-scale long-tailed video recognition
- Exploring classification equilibrium in long-tailed object detection
- GistNet: a geometric structure transfer network for long-tailed recognition
- FASA: Feature augmentation and sampling adaptation for long-tailed instance segmentation
- ACE: Ally complementary experts for solving long-tailed recognition in one-shot
- Influence-Balanced Loss for Imbalanced Visual Classification - Loss) |
- Re-distributing biased pseudo labels for semi-supervised semantic segmentation: A baseline investigation - Lab/DARS) |
- Self supervision to distillation for long-tailed visual recognition - NJU/SSD-LT) |
- Distilling virtual examples for long-tailed recognition
- MosaicOS: A simple and effective use of object-centric images for long-tailed object detection
- Parametric contrastive learning - research/Parametric-Contrastive-Learning) |
- Distributional robustness loss for long-tail learning - LT) |
- Learning of visual relations: The devil is in the tails
- Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
- Self-Damaging Contrastive Learning - Group/SDCLR) |
- Delving into deep imbalanced regression - regression) |
- Long-tailed multi-label visual recognition by collaborative training on uniform and re-balanced samplings
- Equalization loss v2: A new gradient balance approach for long-tailed object detection
- Seesaw loss for long-tailed instance segmentation - mmlab/mmdetection) |
- Adaptive class suppression loss for long-tail object detection - IVA-Lab/ACSL) |
- PML: Progressive margin loss for long-tailed age classification
- Disentangling label distribution for long-tailed visual recognition
- Adversarial robustness under long-tailed distribution - Tail) |
- Distribution alignment: A unified framework for long-tail visual recognition - BaseDetection/DisAlign) |
- Improving calibration for long-tailed recognition - research/MiSLAS) |
- CReST: A class-rebalancing self-training framework for imbalanced semi-supervised learning - research/crest) |
- Conceptual 12M: Pushing web-scale image-text pre-training to recognize long-tail visual concepts - research-datasets/conceptual-12m) |
- RSG: A simple but effective module for learning imbalanced datasets - Wang/RSG) |
- MetaSAug: Meta semantic augmentation for long-tailed visual recognition - DA/MetaSAug) |
- Contrastive learning based hybrid networks for long-tailed image classification
- Unsupervised discovery of the long-tail in instance segmentation using hierarchical self-supervision
- Long-tail learning via logit adjustment - research/google-research/tree/master/logit_adjustment) |
- Long-tailed recognition by routing diverse distribution-aware experts - xwang/RIDE-LongTailRecognition) |
- Exploring balanced feature spaces for representation learning
- MosaicOS: A simple and effective use of object-centric images for long-tailed object detection
- GistNet: a geometric structure transfer network for long-tailed recognition
- Image-Level or Object-Level? A Tale of Two Resampling Strategies for Long-Tailed Detection
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2022
- Relieving long-tailed instance segmentation via pairwise class balance - research/PCB) |
- Long-tail recognition via compositional knowledge transfer
- Nested collaborative learning for long-tailed visual recognition
- Long-tailed recognition via weight balancing - weight-balancing) |
- Self-supervised aggregation of diverse experts for test-agnostic long-tailed recognition - AgnosticLT) |
- SoLar: Sinkhorn label refinery for imbalanced partial-label learning
- Do we really need a learnable classifier at the end of deep neural network?
- Maximum class separation as inductive bias in one matrix - separation-as-inductive-bias) | |
- Escaping saddle points for effective generalization on class-imbalanced data - iisc/Saddle-LongTail) | |
- Breadcrumbs: Adversarial class-balanced sampling for long-tailed recognition - SVCL/Breadcrumbs) |
- Constructing balance from imbalance for long-tailed image recognition
- Tackling long-tailed category distribution under domain shifts - ds) |
- Improving GANs for long-tailed data through group spectral regularization - iisc/gSRGAN) |
- Learning class-wise visual-linguistic representation for long-tailed visual recognition - LTR) |
- Learning with free object segments for long-tailed instance segmentation
- SAFA: Sample-adaptive feature augmentation for long-tailed image classification
- On multi-domain long-tailed recognition, imbalanced domain generalization, and beyond - domain-imbalance) |
- Invariant feature learning for generalized long-tailed classification - Long-Tailed-Benchmarks.pytorch) |
- Towards calibrated hyper-sphere representation via distribution overlap coefficient for long-tailed learning - OP) |
- Long-tailed instance segmentation using Gumbel optimized loss
- Long-tailed class incremental learning - Tailed-CIL) |
- Identifying hard noise in long-tailed sample distribution - Framework) |
- Relieving long-tailed instance segmentation via pairwise class balance - research/PCB) |
- The majority can help the minority: Context-rich minority oversampling for long-tailed classification - ai/cmo) |
- Long-tail recognition via compositional knowledge transfer
- BatchFormer: Learning to explore sample relationships for robust representation learning
- Nested collaborative learning for long-tailed visual recognition
- Long-tailed recognition via weight balancing - weight-balancing) |
- Class-balanced pixel-level self-labeling for domain adaptive semantic segmentation
- Killing two birds with one stone: Efficient and robust training of face recognition CNNs by partial FC
- Optimal transport for long-tailed recognition with learnable cost matrix
- Do deep networks transfer invariances across classes? - invariance-transfer) |
- Self-supervised learning is more robust to dataset imbalance
- BatchFormer: Learning to explore sample relationships for robust representation learning
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2023
- Long-tailed recognition by mutual information maximization between latent features and ground-truth labels - tailed-recognition) |
- Large language models struggle to learn long-tail knowledge
- Feature directions matter: Long-tailed learning via rotated balanced representation
- Wrapped Cauchy distributed angular softmax for long-tailed visual recognition
- Rethinking image super resolution from long-tailed distribution learning perspective
- Transfer knowledge from head to tail: Uncertainty calibration under long-tailed distribution
- Towards realistic long-tailed semi-supervised learning: Consistency is all you need
- Global and local mixture consistency cumulative learning for long-tailed visual recognitions - yangpeng/GLMC) |
- Long-tailed visual recognition via self-heterogeneous integration with knowledge excavation - 06/SHIKE) |
- Balancing logit variation for long-tailed semantic segmentation
- Use your head: Improving long-tail video recognition
- FCC: Feature clusters compression for long-tailed visual recognition
- FEND: A future enhanced distribution-aware contrastive learning framework for long-tail trajectory prediction
- SuperDisco: Super-class discovery improves visual recognition for the long-tail
- Class-conditional sharpness-aware minimization for deep long-tailed recognition - SAM) |
- Balanced product of calibrated experts for long-tailed recognition - CalibratedLT) |
- No one left behind: Improving the worst categories in long-tailed learning
- On the effectiveness of out-of-distribution data in self-supervised long-tail learning
- LPT: Long-tailed prompt tuning for image classification
- Long-tailed partial label learning via dynamic rebalancing - SJTU/RECORDS-LTPLL) |
- Delving into semantic scale imbalance
- INPL: Pseudo-labeling the inliers first for imbalanced semi-supervised learning
- CUDA: Curriculum of data augmentation for long-tailed recognition
- Long-tailed learning requires feature learning
- Decoupled training for long-tailed classification with stochastic representations
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2020
- Balanced meta-softmax for long-taield visual recognition - ren/BalancedMetaSoftmax) |
- Posterior recalibration for imbalanced datasets - RIPL/UNO-IC) |
- Long-tailed classification by keeping the good and removing the bad momentum causal effect - Tailed-Recognition.pytorch) |
- Rethinking the value of labels for improving classimbalanced learning - semi-self) |
- The devil is in classification: A simple framework for long-tail instance segmentation
- Imbalanced continual learning with partitioning reservoir sampling
- Distribution-balanced loss for multi-label classification in long-tailed datasets
- Feature space augmentation for long-tailed data
- Learning from multiple experts: Self-paced knowledge distillation for long-tailed classification
- Solving long-tailed recognition with deep realistic taxonomic classifier - RTC) |
- Learning to segment the tail
- BBN: Bilateral-branch network with cumulative learning for long-tailed visual recognition - Nanjing/BBN) |
- Overcoming classifier imbalance for long-tail object detection with balanced group softmax
- Rethinking class-balanced methods for long-tailed visual recognition from a domain adaptation perspective
- Equalization loss for long-tailed object recognition
- Domain balancing: Face recognition on long-tailed domains
- M2m: Imbalanced classification via majorto-minor translation
- Deep representation learning on long-tailed data: A learnable embedding augmentation perspective
- Inflated episodic memory with region self-attention for long-tailed visual recognition
- Decoupling representation and classifier for long-tailed recognition - balancing) |
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2019
- Meta-weight-net: Learning an explicit mapping for sample weighting - weight-net) |
- Learning imbalanced datasets with label-distribution-aware margin loss - DRW) |
- Dynamic curriculum learning for imbalanced data classification
- Class-balanced loss based on effective number of samples - balanced-loss) |
- Striking the right balance with uncertainty
- Feature transfer learning for face recognition with under-represented data
- Unequal-training for deep face recognition with long-tailed noisy data - Training-for-Deep-Face-Recognition-with-Long-Tailed-Noisy-Data) |
- Large-scale long-tailed recognition in an open world - OLTR) |
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2018
- Large scale fine-grained categorization and domain-specific transfer learning - inaturalist-transfer) |
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2017
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2016
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5. Other Resources
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(2) More discussions on cost-sensitive losses
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