CVPR 2019

Detect-to-Retrieve: Efficient Regional Aggregation for Image Search

CVPR 2019 tensorflow/models

Then, we demonstrate how a trained landmark detector, using our new dataset, can be leveraged to index image regions and improve retrieval accuracy while being much more efficient than existing regional methods.

IMAGE RETRIEVAL

FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation

CVPR 2019 tensorflow/models

Many of the recent successful methods for video object segmentation (VOS) are overly complicated, heavily rely on fine-tuning on the first frame, and/or are slow, and are hence of limited practical use.

SEMANTIC SEGMENTATION VIDEO OBJECT SEGMENTATION VIDEO SEMANTIC SEGMENTATION

A Style-Based Generator Architecture for Generative Adversarial Networks

CVPR 2019 NVlabs/stylegan

We propose an alternative generator architecture for generative adversarial networks, borrowing from style transfer literature.

IMAGE GENERATION

Region Proposal by Guided Anchoring

CVPR 2019 open-mmlab/mmdetection

State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios.

OBJECT DETECTION

Mask Scoring R-CNN

CVPR 2019 open-mmlab/mmdetection

In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks.

INSTANCE SEGMENTATION SEMANTIC SEGMENTATION

Hybrid Task Cascade for Instance Segmentation

CVPR 2019 open-mmlab/mmdetection

In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation.

 SOTA for Instance Segmentation on COCO (using extra training data)

INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION

Deformable ConvNets v2: More Deformable, Better Results

CVPR 2019 open-mmlab/mmdetection

The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects.

INSTANCE SEGMENTATION OBJECT DETECTION SEMANTIC SEGMENTATION

Grid R-CNN

CVPR 2019 open-mmlab/mmdetection

This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection.

OBJECT DETECTION OBJECT LOCALIZATION

Libra R-CNN: Towards Balanced Learning for Object Detection

CVPR 2019 open-mmlab/mmdetection

In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level.

OBJECT DETECTION

Semantic Image Synthesis with Spatially-Adaptive Normalization

CVPR 2019 NVlabs/SPADE

We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout.

IMAGE-TO-IMAGE TRANSLATION