文章 - 125

2020
Non-local Neural Networks
Resisting the Distracting-factors in Pedestrian Detection
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective
Cross-domain Object Detection through Coarse-to-Fine Feature Adaptation
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
Meta-RCNN: Meta learning for few-shot object detection
Diversity Transfer Network for Few-Shot Learning
Bounding Box Regression with Uncertainty for Accurate Object Detection
CenterMask: single shot instance segmentation with point representation
CenterMask : Real-Time Anchor-Free Instance Segmentation
LSTD: A low-shot transfer detector for object detection
Meta-Learning to Detect Rare Objects
Large-Margin Softmax Loss for Convolutional Neural Networks
FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings
Prototypical networks for few-shot learning
Meta-Transfer Learning for Few-Shot Learning
Learning to Compare: Relation Network for Few-Shot Learning
SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning
Few-Shot Object Detection with Attention-RPN and Multi-Relation Detector
RepMet: Representative-based metric learning for classification and few-shot object detection
Conditional Convolutions for Instance Segmentation
Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning
Incremental Few-Shot Object Detection
Objects as Points
Few-shot Object Detection via Feature Reweighting
Few-shot Adaptive Faster R-CNN
Context Contrasted Feature and Gated Multi-scale Aggregation for Scene Segmentation
Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection
CityPersons: A Diverse Dataset for Pedestrian Detection
Object as Distribution