FCOS: Fully Convolutional One-Stage Object Detection

ICCV 2019 aim-uofa/adet

By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training.

OBJECT DETECTION SEMANTIC SEGMENTATION

SOLO: Segmenting Objects by Locations

10 Dec 2019aim-uofa/adet

We present a new, embarrassingly simple approach to instance segmentation in images.

INSTANCE SEGMENTATION SEMANTIC SEGMENTATION

DirectPose: Direct End-to-End Multi-Person Pose Estimation

18 Nov 2019aim-uofa/adet

We propose the first direct end-to-end multi-person pose estimation framework, termed DirectPose.

MULTI-PERSON POSE ESTIMATION

BlendMask: Top-Down Meets Bottom-Up for Instance Segmentation

2 Jan 2020aim-uofa/adet

The proposed BlendMask can effectively predict dense per-pixel position-sensitive instance features with very few channels, and learn attention maps for each instance with merely one convolution layer, thus being fast in inference.

INSTANCE SEGMENTATION SEMANTIC SEGMENTATION

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