• How to write good papers

  • Good Citizen of CVPR at CVPR 2018 in Salt Lake City, Utah
  • How to write a great research paper by Microsoft Research

• Application

  • Face Recognition
    [1][2][6]
  • Face Super-Resolution
    [11]
  • Image Caption
    [3]
  • Person Re-identification
    [4][7][10][12][14]
  • Object Detection
    [5]
  • Image/Instance Segmentation
    [8][13]

• Method

  • Loss Design
    [1][5]
  • Attention
    [3][13]
  • GAN
    [4][7][8][9][14]
  • Domain Adaption
    [4][6][7][9]
  • Clustering
    [12]

[1] ECCV'18: Orthogonal Deep Features Decomposition for Age-Invariant Face Recognition | Tencent AI
[2] ECCV'18: GridFace: Face Rectification via Learning Local Homography Transformations | Face++ | Resource
[3] CVPR'17: SCA-CNN: Spatial and Channel-wise Attention in Convolutional Networks for Image Captioning | NUS | Resource | Code
[4] ECCV'18: Generalizing A Person Retrieval Model Hetero- and Homogeneously | ANU | Code
[5] CVPR'18: Repulsion Loss: Detecting Pedestrians in a Crowd | Face++ | Resource | Code
[6] Arxiv'18: DocFace: Matching ID Document Photos to Selfies | MSU | Code
[7] CVPR'18: Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification | ANU | Code
[8] ACM MM'18: Understanding Humans in Crowded Scenes: Deep Nested Adversarial Learning and A New Benchmark for Multi-Human Parsing | NUS | Resource | Code
[9] NIPS'18: A Unified Feature Disentangler for Multi-Domain Image Translation and Manipulation | NTU, Taiwan | Code
[10] ECCV'18: Person Search via A Mask-Guided Two-Stream CNN Model | NUST | Resource
[11] CVPR'18: FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors | NUST | Resource | Code
[12] AAAA'19: A Bottom-up Clustering Approach to Unsupervised Person Re-identification | UTS
[13] CVPR'16: Attention to Scale: Scale-aware Semantic Image Segmentation | UCLA, Baidu | Code
[14] NIPS'18: FD-GAN: Pose-guided Feature Distilling GAN for Robust Person Re-identification | CUHK, SenseTime | Code

Loss:
CVPR'19: SoDeep: A Sorting Deep Net to Learn Ranking Loss Surrogates

Continues Learning:
CVPR'19: Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning

Noise Label:
CVPR'19: Weakly Supervised Image Classification Through Noise Regularization
CVPR'19: Noise-Tolerant Paradigm for Training Face Recognition CNNs
CVPR'19: On Stabilizing Generative Adversarial Training With Noise
CVPR'19: Probabilistic End-To-End Noise Correction for Learning With Noisy Labels
CVPR'19: MetaCleaner: Learning to Hallucinate Clean Representations for Noisy-Labeled Visual Recognition

Unbalance Data:
CVPR'19: Learning Not to Learn: Training Deep Neural Networks With Biased Data
CVPR'19: Class-Balanced Loss Based on Effective Number of Samples

Domain Adaptation:
CVPR'19: Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss
CVPR'19: Sliced Wasserstein Generative Models
CVPR'19: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation

Self-supervised:
CVPR'19: Self-Supervised GANs via Auxiliary Rotation Loss
CVPR'19: Self-Supervised Representation Learning by Rotation Feature Decoupling

memeda:
CVPR'19: Rethinking the Evaluation of Video Summaries