119 citations · 280 across the 10 of their papers we have counts for
12 papers · 1 filter
Point-Level Region Contrast for Object Detection Pre-Training
Yutong Bai, Xinlei Chen, Alexander Kirillov +2
In this work we present point-level region contrast, a self-supervised pre-training approach for the task of object detection. This approach is motivated by the two key factors in…
Boundary IoU: Improving Object-Centric Image Segmentation Evaluation
Bowen Cheng, Ross Girshick, Piotr Dollár +2
We present Boundary IoU (Intersection-over-Union), a new segmentation evaluation measure focused on boundary quality. We perform an extensive analysis across different error types…
A Mask-RCNN Baseline for Probabilistic Object Detection
Phil Ammirato, Alexander C. Berg
The Probabilistic Object Detection Challenge evaluates object detection methods using a new evaluation measure, Probability-based Detection Quality (PDQ), on a new synthetic image…
IMP: Instance Mask Projection for High Accuracy Semantic Segmentation of Things
Cheng-Yang Fu, Tamara L. Berg, Alexander C. Berg
In this work, we present a new operator, called Instance Mask Projection (IMP), which projects a predicted Instance Segmentation as a new feature for semantic segmentation. It also…
Low-Power Computer Vision: Status, Challenges, Opportunities
Sergei Alyamkin, Matthew Ardi, Alexander C. Berg +41
Computer vision has achieved impressive progress in recent years. Meanwhile, mobile phones have become the primary computing platforms for millions of people. In addition to mobile…
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
Chen Feng, Tao Sheng, Zhiyu Liang +9
The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual competition started in 2015 that encourages joint hardware and software solutions for computer vision systems wi…