Papers
Natural Language Processing
Distributed Word Representations
- 2017-11
- Faruqui and Dyer – 2014 – Improving vector space word representations using multilingual correlation [pdf] [note]
- Maaten and Hinton – 2008 – Visualizing data using t-SNE [pdf] [pdf (annotated)] [note]
- Ling et al. – 2015 – Finding function in form: Compositional character models for open vocabulary word representation [pdf] [pdf (annotated)] [note]
- Bojanowski et al. – 2016 – Enriching word vectors with subword information [pdf] [pdf (annotated)] [note]
- 2017-12
- Bengio and Senécal – 2003 – Quick Training of Probabilistic Neural Nets by Importance Sampling [pdf] [pdf(annotated)] [note]
- references
Distributed Sentence Representations
- 2017-11
- Le and Mikolov – 2014 – Distributed representations of sentences and documents [pdf] [pdf (annotated)] [note]
- 2018-12
- Li and Hovy – 2014 – A Model of Coherence Based on Distributed Sentence Representation [pdf] [pdf (annotated)] [note]
- Kiros et al. – 2015 – Skip-Thought Vectors [pdf] [pdf (annotated)] [note]
- Hill et al. – 2016 – Learning Distributed Representations of Sentences from Unlabelled Data [pdf] [pdf (annotated)] [note]
- Arora et al. – 2016 – A simple but tough-to-beat baseline for sentence embeddings [pdf] [pdf (annotated)] [note]
- Pagliardini et al. – 2017 – Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features (sent2vec) [pdf] [pdf (annotated)] [note]
- Logeswaran et al. – 2018 – An efficient framework for learning sentence representations (Quick-Thought Vectors) [pdf] [pdf (annotated)] [note]
- 2019-01
- Wieting et al. – 2015 – Towards universal paraphrastic sentence embeddings [pdf] [pdf (annotated)] [note]
- Adi et al. – 2016 – Fine-grained Analysis of Sentence Embeddings Using Auxiliary Prediction Tasks [pdf] [pdf (annotated)] [note]
- Conneau et al. – 2017 – Supervised Learning of Universal Sentence Representations from Natural Language Inference Data (InferSent) [pdf] [pdf (annotated)] [note]
- Cer et al. – 2018 – Universal Sentence Encoder [pdf] [pdf (annotated)] [note]
- references
Entity Recognition
- 2018-10
- Lample et al. – 2016 – Neural Architectures for Named Entity Recognition [pdf]
- Ma and Hovy – 2016 – End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF [pdf]
- Yang et al. – 2017 – Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks [pdf]
- Peters et al. – 2017 – Semi-supervised sequence tagging with bidirectional language models [pdf]
- Shang et al. – 2018 – Learning Named Entity Tagger using Domain-Specific Dictionary [pdf]
- references
Language Model
- 2017-11
- 2019-02
- Peters et al. – 2018- Deep contextualized word representations(ELMo) [pdf] [note]
- Howard and Ruder – 2018 – Universal language model fine-tuning for text classification(ULMFit) [pdf]
- Radford et al. – 2018 – Improving language understanding by generative pre-training [pdf]
- Devlin et al. – 2018 – Bert: Pre-training of deep bidirectional transformers for language understanding [pdf]
- references
- Blog:The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)
- ELMo
- Quick Start: Training an IMDb sentiment model with ULMFiT
- finetune-transformer-lm: Code and model for the paper “Improving Language Understanding by Generative Pre-Training”
- awesome-bert: bert nlp papers, applications and github resources , BERT 相关论文和 github 项目
Machine Translation
- 2017-12
- Oda et al. – 2017 – Neural Machine Translation via Binary Code Predict [pdf] [note]
- Kalchbrenner et al. – 2016 – Neural machine translation in linear time [pdf] [pdf (annotated)] [note]
- 2018-05
- Sutskever et al. – 2014 – Sequence to Sequence Learning with Neural Networks [pdf]
- Cho et al. – 2014 – Learning Phrase Representations using RNN Encoder-Decoder for NMT [pdf]
- Bahdanau et al. – 2014 – NMT by Jointly Learning to Align and Translate [pdf]
- Luong et al. – 2015 – Effective Approaches to Attention-based NMT [pdf]
- 2018-06
- Gehring et al. – 2017 – Convolutional sequence to sequence learning [pdf]
- Vaswani et al. – 2017 – Attention is all you need [pdf] [note1:The Illustrated Transformer] [note2:The Annotated Transformer]
- references
Question Answering
- 2018-03
- 2018-04
- Clark and Gardner. – 2017 – Simple and Effective Multi-Paragraph Reading Comprehension [pdf]
- Wang et al. – 2017 – Gated Self-Matching Networks for Reading Comprehension and Question Answering [pdf]
- Yu et al. – 2018 – QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension [pdf]
- references
Recommendation Systems
- 2019-05
- Rendle S. – 2010 – Factorization machines [pdf] [note]
- Cheng et al. – 2016 – Wide & Deep Learning for Recommender Systems [pdf]
- Guo et al. – 2017 – DeepFM: A Factorization-Machine based Neural Network for CTR Prediction [pdf]
- He and Chua. – 2017 – Neural Factorization Machines for Sparse Predictive Analytics [pdf]
Relation Extraction
- 2018-08
- Mintz et al. – 2009 – Distant supervision for relation extraction without labeled data [pdf]
- Zeng et al. – 2015 – Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks [pdf]
- Zhou et al. – 2016 – Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification [pdf]
- Lin et al. – 2016 – Neural Relation Extraction with Selective Attention over Instances [pdf]
- 2018-09
- references
Sentences Matching
- 2017-12
- Hu et al. – 2014 – Convolutional neural network architectures for Matching Natural Language Sentences [pdf] [pdf (annotated)] [note]
- 2018-07
- Nie and Bansal – 2017 – Shortcut-Stacked Sentence Encoders for Multi-Domain Inference [pdf] [note]
- Wang et al. – 2017 – Bilateral Multi-Perspective Matching for Natural Language Sentences [pdf] [note]
- Tay et al. – 2017 – A Compare-Propagate Architecture with Alignment Factorization for Natural Language Inference [pdf]
- Chen et al. – 2017 – Enhanced LSTM for Natural Language Inference [pdf] [note]
- Ghaeini et al. – 2018 – DR-BiLSTM: Dependent Reading Bidirectional LSTM for Natural Language Inference [pdf]
- references
Text Classification
- 2017-09
- Joulin et al. – 2016 – Bag of tricks for efficient text classification [pdf] [pdf (annotated)] [note]
- 2017-10
- Kim – 2014 – Convolutional neural networks for sentence classification [pdf] [pdf (annotated)] [note]
- Zhang and Wallace – 2015 – A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification [pdf] [pdf (annotated)] [note]
- Zhang et al. – 2015 – Character-level convolutional networks for text classification [pdf] [pdf (annotated)] [note]
- Lai et al. – 2015 – Recurrent Convolutional Neural Networks for Text Classification [pdf] [pdf (annotated)] [note]
- Yang et al. – 2016 – Hierarchical attention networks for document classification [pdf]
- 2017-11
- Iyyer et al. – 2015 – Deep unordered composition rivals syntactic methods for Text Classification [pdf] [pdf (annotated)] [note]
- 2019-04 (Aspect level sentiment classification)
- Wang et al. – 2016 – Attention-based LSTM for aspect-level sentiment classification [pdf]
- Tang et al. – 2016 – Aspect level sentiment classification with deep memory network [pdf]
- Chen et al. – 2017 – Recurrent Attention Network on Memory for Aspect Sentiment Analysis [pdf]
- Xue and Li – 2018 – Aspect Based Sentiment Analysis with Gated Convolutional Networks [pdf]
Computer Vision
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[R-CNN] Rich feature hierarchies for accurate object detection and semantic segmentation | [CVPR’ 14] |
[pdf]
[official code - caffe]
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[OverFeat] OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks | [ICLR’ 14] |
[pdf]
[official code - torch]
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[MultiBox] Scalable Object Detection using Deep Neural Networks | [CVPR’ 14] |
[pdf]
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[SPP-Net] Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition | [ECCV’ 14] |
[pdf]
[official code - caffe]
[unofficial code - keras]
[unofficial code - tensorflow]
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Improving Object Detection with Deep Convolutional Networks via Bayesian Optimization and Structured Prediction | [CVPR’ 15] |
[pdf]
[official code - matlab]
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[MR-CNN] Object detection via a multi-region & semantic segmentation-aware CNN model | [ICCV’ 15] |
[pdf]
[official code - caffe]
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[DeepBox] DeepBox: Learning Objectness with Convolutional Networks | [ICCV’ 15] |
[pdf]
[official code - caffe]
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[AttentionNet] AttentionNet: Aggregating Weak Directions for Accurate Object Detection | [ICCV’ 15] |
[pdf]
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[Fast R-CNN] Fast R-CNN | [ICCV’ 15] |
[pdf]
[official code - caffe]
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[DeepProposal] DeepProposal: Hunting Objects by Cascading Deep Convolutional Layers | [ICCV’ 15] |
[pdf]
[official code - matconvnet]
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[Faster R-CNN, RPN] Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks | [NIPS’ 15] |
[pdf]
[official code - caffe]
[unofficial code - tensorflow]
[unofficial code - pytorch]
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[YOLO v1] You Only Look Once: Unified, Real-Time Object Detection | [CVPR’ 16] |
[pdf]
[official code - c]
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[G-CNN] G-CNN: an Iterative Grid Based Object Detector | [CVPR’ 16] |
[pdf]
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[AZNet] Adaptive Object Detection Using Adjacency and Zoom Prediction | [CVPR’ 16] |
[pdf]
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[ION] Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks | [CVPR’ 16] |
[pdf]
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[HyperNet] HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection | [CVPR’ 16] |
[pdf]
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[OHEM] Training Region-based Object Detectors with Online Hard Example Mining | [CVPR’ 16] |
[pdf]
[official code - caffe]
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[CRAPF] CRAFT Objects from Images | [CVPR’ 16] |
[pdf]
[official code - caffe]
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[MPN] A MultiPath Network for Object Detection | [BMVC’ 16] |
[pdf]
[official code - torch]
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[SSD] SSD: Single Shot MultiBox Detector | [ECCV’ 16] |
[pdf]
[official code - caffe]
[unofficial code - tensorflow]
[unofficial code - pytorch]
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[GBDNet] Crafting GBD-Net for Object Detection | [ECCV’ 16] |
[pdf]
[official code - caffe]
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[CPF] Contextual Priming and Feedback for Faster R-CNN | [ECCV’ 16] |
[pdf]
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[MS-CNN] A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection | [ECCV’ 16] |
[pdf]
[official code - caffe]
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[R-FCN] R-FCN: Object Detection via Region-based Fully Convolutional Networks | [NIPS’ 16] |
[pdf]
[official code - caffe]
[unofficial code - caffe]
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[PVANET] PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection | [NIPSW’ 16] |
[pdf]
[official code - caffe]
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[DeepID-Net] DeepID-Net: Deformable Deep Convolutional Neural Networks for Object Detection | [PAMI’ 16] |
[pdf]
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[NoC] Object Detection Networks on Convolutional Feature Maps | [TPAMI’ 16] |
[pdf]
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[DSSD] DSSD : Deconvolutional Single Shot Detector | [arXiv’ 17] |
[pdf]
[official code - caffe]
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[TDM] Beyond Skip Connections: Top-Down Modulation for Object Detection | [CVPR’ 17] |
[pdf]
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[FPN] Feature Pyramid Networks for Object Detection | [CVPR’ 17] |
[pdf]
[unofficial code - caffe]
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[YOLO v2] YOLO9000: Better, Faster, Stronger | [CVPR’ 17] |
[pdf]
[official code - c]
[unofficial code - caffe]
[unofficial code - tensorflow]
[unofficial code - tensorflow]
[unofficial code - pytorch]
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[RON] RON: Reverse Connection with Objectness Prior Networks for Object Detection | [CVPR’ 17] |
[pdf]
[official code - caffe]
[unofficial code - tensorflow]
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[RSA] Recurrent Scale Approximation for Object Detection in CNN | | [ICCV’ 17] |
[pdf]
[official code - caffe]
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[DCN] Deformable Convolutional Networks | [ICCV’ 17] |
[pdf]
[official code - mxnet]
[unofficial code - tensorflow]
[unofficial code - pytorch]
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[DeNet] DeNet: Scalable Real-time Object Detection with Directed Sparse Sampling | [ICCV’ 17] |
[pdf]
[official code - theano]
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[CoupleNet] CoupleNet: Coupling Global Structure with Local Parts for Object Detection | [ICCV’ 17] |
[pdf]
[official code - caffe]
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[RetinaNet] Focal Loss for Dense Object Detection | [ICCV’ 17] |
[pdf]
[official code - keras]
[unofficial code - pytorch]
[unofficial code - mxnet]
[unofficial code - tensorflow]
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[Mask R-CNN] Mask R-CNN | [ICCV’ 17] |
[pdf]
[official code - caffe2]
[unofficial code - tensorflow]
[unofficial code - tensorflow]
[unofficial code - pytorch]
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[DSOD] DSOD: Learning Deeply Supervised Object Detectors from Scratch | [ICCV’ 17] |
[pdf]
[official code - caffe]
[unofficial code - pytorch]
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[SMN] Spatial Memory for Context Reasoning in Object Detection | [ICCV’ 17] |
[pdf]
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[Light-Head R-CNN] Light-Head R-CNN: In Defense of Two-Stage Object Detector | [arXiv’ 17] |
[pdf]
[official code - tensorflow]
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[Soft-NMS] Improving Object Detection With One Line of Code | [ICCV’ 17] |
[pdf]
[official code - caffe]
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[YOLO v3] YOLOv3: An Incremental Improvement | [arXiv’ 18] |
[pdf]
[official code - c]
[unofficial code - pytorch]
[unofficial code - pytorch]
[unofficial code - keras]
[unofficial code - tensorflow]
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[ZIP] Zoom Out-and-In Network with Recursive Training for Object Proposal | [IJCV’ 18] |
[pdf]
[official code - caffe]
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[SIN] Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships | [CVPR’ 18] |
[pdf]
[official code - tensorflow]
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[STDN] Scale-Transferrable Object Detection | [CVPR’ 18] |
[pdf]
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[RefineDet] Single-Shot Refinement Neural Network for Object Detection | [CVPR’ 18] |
[pdf]
[official code - caffe]
[unofficial code - chainer]
[unofficial code - pytorch]
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[MegDet] MegDet: A Large Mini-Batch Object Detector | [CVPR’ 18] |
[pdf]
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[DA Faster R-CNN] Domain Adaptive Faster R-CNN for Object Detection in the Wild | [CVPR’ 18] |
[pdf]
[official code - caffe]
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[SNIP] An Analysis of Scale Invariance in Object Detection – SNIP | [CVPR’ 18] |
[pdf]
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[Relation-Network] Relation Networks for Object Detection | [CVPR’ 18] |
[pdf]
[official code - mxnet]
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[Cascade R-CNN] Cascade R-CNN: Delving into High Quality Object Detection | [CVPR’ 18] |
[pdf]
[official code - caffe]
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Finding Tiny Faces in the Wild with Generative Adversarial Network | [CVPR’ 18] |
[pdf]
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[MLKP] Multi-scale Location-aware Kernel Representation for Object Detection | [CVPR’ 18] |
[pdf]
[official code - caffe]
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Cross-Domain Weakly-Supervised Object Detection through Progressive Domain Adaptation | [CVPR’ 18] |
[pdf]
[official code - chainer]
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[Fitness NMS] Improving Object Localization with Fitness NMS and Bounded IoU Loss | [CVPR’ 18] |
[pdf]
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[STDnet] STDnet: A ConvNet for Small Target Detection | [BMVC’ 18] |
[pdf]
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[RFBNet] Receptive Field Block Net for Accurate and Fast Object Detection | [ECCV’ 18] |
[pdf]
[official code - pytorch]
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Zero-Annotation Object Detection with Web Knowledge Transfer | [ECCV’ 18] |
[pdf]
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[CornerNet] CornerNet: Detecting Objects as Paired Keypoints | [ECCV’ 18] |
[pdf]
[official code - pytorch]
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[PFPNet] Parallel Feature Pyramid Network for Object Detection | [ECCV’ 18] |
[pdf]
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[Softer-NMS] Softer-NMS: Rethinking Bounding Box Regression for Accurate Object Detection | [arXiv’ 18] |
[pdf]
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[ShapeShifter] ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector | [ECML-PKDD’ 18] |
[pdf]
[official code - tensorflow]
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[Pelee] Pelee: A Real-Time Object Detection System on Mobile Devices | [NIPS’ 18] |
[pdf]
[official code - caffe]
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[HKRM] Hybrid Knowledge Routed Modules for Large-scale Object Detection | [NIPS’ 18] |
[pdf]
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[MetaAnchor] MetaAnchor: Learning to Detect Objects with Customized Anchors | [NIPS’ 18] |
[pdf]
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[SNIPER] SNIPER: Efficient Multi-Scale Training | [NIPS’ 18] |
[pdf]
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[M2Det] M2Det: A Single-Shot Object Detector based on Multi-Level Feature Pyramid Network | [AAAI’ 19] |
[pdf]
[official code - pytorch]
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[R-DAD] Object Detection based on Region Decomposition and Assembly | [AAAI’ 19] |
[pdf]
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[CAMOU] CAMOU: Learning Physical Vehicle Camouflages to Adversarially Attack Detectors in the Wild | [ICLR’ 19] |
[pdf]
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Feature Intertwiner for Object Detection | [ICLR’ 19] |
[pdf]
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[GIoU] Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression | [CVPR’ 19] |
[pdf]
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Automatic adaptation of object detectors to new domains using self-training | [CVPR’ 19] |
[pdf]
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[Libra R-CNN] Libra R-CNN: Balanced Learning for Object Detection | [CVPR’ 19] |
[pdf]
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Feature Selective Anchor-Free Module for Single-Shot Object Detection | [CVPR’ 19] |
[pdf]
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[ExtremeNet] Bottom-up Object Detection by Grouping Extreme and Center Points | [CVPR’ 19] |
[pdf]
|[official code - pytorch]
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[C-MIL] C-MIL: Continuation Multiple Instance Learning for Weakly Supervised Object Detection
| [CVPR’ 19] |
[pdf]
|[official code - torch]
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[ScratchDet] ScratchDet: Training Single-Shot Object Detectors from Scratch | [CVPR’ 19] |
[pdf]
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Bounding Box Regression with Uncertainty for Accurate Object Detection | [CVPR’ 19] |
[pdf]
|[official code - caffe2]
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Activity Driven Weakly Supervised Object Detection | [CVPR’ 19] |
[pdf]
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Towards Accurate One-Stage Object Detection with AP-Loss | [CVPR’ 19] |
[pdf]
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Strong-Weak Distribution Alignment for Adaptive Object Detection | [CVPR’ 19] |
[pdf]
|[official code - pytorch]
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[NAS-FPN] NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection | [CVPR’ 19] |
[pdf]
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[Adaptive NMS] Adaptive NMS: Refining Pedestrian Detection in a Crowd | [CVPR’ 19] |
[pdf]
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Point in, Box out: Beyond Counting Persons in Crowds | [CVPR’ 19] |
[pdf]
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Locating Objects Without Bounding Boxes | [CVPR’ 19] |
[pdf]
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Sampling Techniques for Large-Scale Object Detection from Sparsely Annotated Objects | [CVPR’ 19] |
[pdf]
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Towards Universal Object Detection by Domain Attention | [CVPR’ 19] |
[pdf]
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Exploring the Bounds of the Utility of Context for Object Detection | [CVPR’ 19] |
[pdf]
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What Object Should I Use? – Task Driven Object Detection | [CVPR’ 19] |
[pdf]
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Dissimilarity Coefficient based Weakly Supervised Object Detection | [CVPR’ 19] |
[pdf]
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Adapting Object Detectors via Selective Cross-Domain Alignment | [CVPR’ 19] |
[pdf]
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Fully Quantized Network for Object Detection | [CVPR’ 19] |
[pdf]
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Distilling Object Detectors with Fine-grained Feature Imitation | [CVPR’ 19] |
[pdf]
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Multi-task Self-Supervised Object Detection via Recycling of Bounding Box Annotations | [CVPR’ 19] |
[pdf]
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[Reasoning-RCNN] Reasoning-RCNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection | [CVPR’ 19] |
[pdf]
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Arbitrary Shape Scene Text Detection with Adaptive Text Region Representation | [CVPR’ 19] |
[pdf]
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Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors | [CVPR’ 19] |
[pdf]
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Spatial-aware Graph Relation Network for Large-scale Object Detection | [CVPR’ 19] |
[pdf]
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[MaxpoolNMS] MaxpoolNMS: Getting Rid of NMS Bottlenecks in Two-Stage Object Detectors | [CVPR’ 19] |
[pdf]
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You reap what you sow: Generating High Precision Object Proposals for Weakly-supervised Object Detection | [CVPR’ 19] |
[pdf]
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Object detection with location-aware deformable convolution and backward attention filtering | [CVPR’ 19] |
[pdf]
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Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection | [CVPR’ 19] |
[pdf]
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[GFR] Improving Object Detection from Scratch via Gated Feature Reuse | [BMVC’ 19] |
[pdf]
|[official code - pytorch]
-
[Cascade RetinaNet] Cascade RetinaNet: Maintaining Consistency for Single-Stage Object Detection | [BMVC’ 19] |
[pdf]
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Soft Sampling for Robust Object Detection | [BMVC’ 19] |
[pdf]
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Multi-adversarial Faster-RCNN for Unrestricted Object Detection | [ICCV’ 19] |
[pdf]
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Towards Adversarially Robust Object Detection | [ICCV’ 19] |
[pdf]
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[Cap2Det] Cap2Det: Learning to Amplify Weak Caption Supervision for Object Detection | [ICCV’ 19] |
[pdf]
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[Gaussian YOLOv3] Gaussian YOLOv3: An Accurate and Fast Object Detector using Localization Uncertainty for Autonomous Driving | [ICCV’ 19] |
[pdf]
Reinforcement Learning
- Human Level Control through Deep Reinforcement Learning
- Asynchronous Methods for Deep Reinforcement Learning
- Deep Reinforcement Learning with Double Q-learning
- Dueling Network Architectures for Deep Reinforcement Learning
- Playing Atari with Deep Reinforcement Learning
- HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
- Deterministic Policy Gradient Algorithms
- Continuous control with deep reinforcement learning
- High-Dimensional Continuous Control Using Generalized Advantage Estimation
- Hybrid Reward Architecture for Reinforcement Learning
- Trust Region Policy Optimization
- Proximal Policy Optimization Algorithms
- Emergence of Locomotion Behaviours in Rich Environments
- Action-Conditional Video Prediction using Deep Networks in Atari Games
- A Distributional Perspective on Reinforcement Learning
- Distributional Reinforcement Learning with Quantile Regression
- The Option-Critic Architecture
- Some hyper-parameters are from DeepMind Control Suite, OpenAI Baselines and Ilya Kostrikov