Search Results for author: Piotr Dollár

Found 33 papers, 29 papers with code

Early Convolutions Help Transformers See Better

1 code implementation NeurIPS 2021 Tete Xiao, Mannat Singh, Eric Mintun, Trevor Darrell, Piotr Dollár, Ross Girshick

To test whether this atypical design choice causes an issue, we analyze the optimization behavior of ViT models with their original patchify stem versus a simple counterpart where we replace the ViT stem by a small number of stacked stride-two 3*3 convolutions.

Boundary IoU: Improving Object-Centric Image Segmentation Evaluation

2 code implementations CVPR 2021 Bowen Cheng, Ross Girshick, Piotr Dollár, Alexander C. Berg, Alexander Kirillov

We perform an extensive analysis across different error types and object sizes and show that Boundary IoU is significantly more sensitive than the standard Mask IoU measure to boundary errors for large objects and does not over-penalize errors on smaller objects.

Image Segmentation Object +2

Fast and Accurate Model Scaling

4 code implementations CVPR 2021 Piotr Dollár, Mannat Singh, Ross Girshick

This leads us to propose a simple fast compound scaling strategy that encourages primarily scaling model width, while scaling depth and resolution to a lesser extent.

Are Labels Necessary for Neural Architecture Search?

2 code implementations ECCV 2020 Chenxi Liu, Piotr Dollár, Kaiming He, Ross Girshick, Alan Yuille, Saining Xie

Existing neural network architectures in computer vision -- whether designed by humans or by machines -- were typically found using both images and their associated labels.

Neural Architecture Search

LVIS: A Dataset for Large Vocabulary Instance Segmentation

2 code implementations CVPR 2019 Agrim Gupta, Piotr Dollár, Ross Girshick

We plan to collect ~2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images.

Instance Segmentation Object +4

On Network Design Spaces for Visual Recognition

4 code implementations ICCV 2019 Ilija Radosavovic, Justin Johnson, Saining Xie, Wan-Yen Lo, Piotr Dollár

Compared to current methodologies of comparing point and curve estimates of model families, distribution estimates paint a more complete picture of the entire design landscape.

Neural Architecture Search

TensorMask: A Foundation for Dense Object Segmentation

2 code implementations ICCV 2019 Xinlei Chen, Ross Girshick, Kaiming He, Piotr Dollár

To formalize this, we treat dense instance segmentation as a prediction task over 4D tensors and present a general framework called TensorMask that explicitly captures this geometry and enables novel operators on 4D tensors.

Instance Segmentation Object +4

Panoptic Feature Pyramid Networks

11 code implementations CVPR 2019 Alexander Kirillov, Ross Girshick, Kaiming He, Piotr Dollár

In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks.

Instance Segmentation Panoptic Segmentation +2

Rethinking ImageNet Pre-training

1 code implementation ICCV 2019 Kaiming He, Ross Girshick, Piotr Dollár

We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization.

Instance Segmentation object-detection +2

Data Distillation: Towards Omni-Supervised Learning

4 code implementations CVPR 2018 Ilija Radosavovic, Piotr Dollár, Ross Girshick, Georgia Gkioxari, Kaiming He

We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data.

Keypoint Detection object-detection +1

Learning to Segment Every Thing

3 code implementations CVPR 2018 Ronghang Hu, Piotr Dollár, Kaiming He, Trevor Darrell, Ross Girshick

Most methods for object instance segmentation require all training examples to be labeled with segmentation masks.

Instance Segmentation Segmentation +1

Focal Loss for Dense Object Detection

231 code implementations ICCV 2017 Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, Piotr Dollár

Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training.

Dense Object Detection Knowledge Distillation +5

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

70 code implementations8 Jun 2017 Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, Kaiming He

To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training.

Stochastic Optimization

Detecting and Recognizing Human-Object Interactions

2 code implementations CVPR 2018 Georgia Gkioxari, Ross Girshick, Piotr Dollár, Kaiming He

Our hypothesis is that the appearance of a person -- their pose, clothing, action -- is a powerful cue for localizing the objects they are interacting with.

Human-Object Interaction Detection Object

Mask R-CNN

171 code implementations ICCV 2017 Kaiming He, Georgia Gkioxari, Piotr Dollár, Ross Girshick

Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance.

3D Instance Segmentation Human Part Segmentation +12

Learning Features by Watching Objects Move

1 code implementation CVPR 2017 Deepak Pathak, Ross Girshick, Piotr Dollár, Trevor Darrell, Bharath Hariharan

Given the extensive evidence that motion plays a key role in the development of the human visual system, we hope that this straightforward approach to unsupervised learning will be more effective than cleverly designed 'pretext' tasks studied in the literature.

object-detection Object Detection +1

A MultiPath Network for Object Detection

1 code implementation7 Apr 2016 Sergey Zagoruyko, Adam Lerer, Tsung-Yi Lin, Pedro O. Pinheiro, Sam Gross, Soumith Chintala, Piotr Dollár

To address these challenges, we test three modifications to the standard Fast R-CNN object detector: (1) skip connections that give the detector access to features at multiple network layers, (2) a foveal structure to exploit object context at multiple object resolutions, and (3) an integral loss function and corresponding network adjustment that improve localization.

Instance Segmentation Object +3

Unsupervised Learning of Edges

no code implementations CVPR 2016 Yin Li, Manohar Paluri, James M. Rehg, Piotr Dollár

In this work we present a simple yet effective approach for training edge detectors without human supervision.

Edge Detection Motion Estimation +2

Semantic Amodal Segmentation

2 code implementations CVPR 2017 Yan Zhu, Yuandong Tian, Dimitris Mexatas, Piotr Dollár

Specifically, we create an amodal segmentation of each image: the full extent of each region is marked, not just the visible pixels.

object-detection Object Detection +2

What makes for effective detection proposals?

no code implementations17 Feb 2015 Jan Hosang, Rodrigo Benenson, Piotr Dollár, Bernt Schiele

Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images.

Object object-detection +1

Fast Edge Detection Using Structured Forests

no code implementations20 Jun 2014 Piotr Dollár, C. Lawrence Zitnick

We formulate the problem of predicting local edge masks in a structured learning framework applied to random decision forests.

Edge Detection Image Segmentation +1

Local Decorrelation For Improved Detection

no code implementations4 Jun 2014 Woonhyun Nam, Piotr Dollár, Joon Hee Han

In fact, orthogonal trees with our locally decorrelated features outperform oblique trees trained over the original features at a fraction of the computational cost.

object-detection Object Detection

Microsoft COCO: Common Objects in Context

33 code implementations1 May 2014 Tsung-Yi Lin, Michael Maire, Serge Belongie, Lubomir Bourdev, Ross Girshick, James Hays, Pietro Perona, Deva Ramanan, C. Lawrence Zitnick, Piotr Dollár

We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding.

Instance Segmentation Object +5

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