18.1k citations · 35.5k across the 59 of their papers we have counts for
13 papers · 1 filter
Actions and Attributes from Wholes and Parts
Georgia Gkioxari, Ross Girshick, Jitendra Malik
We investigate the importance of parts for the tasks of action and attribute classification. We develop a part-based approach by leveraging convolutional network features inspired…
Hypercolumns for Object Segmentation and Fine-grained Localization
Bharath Hariharan, Pablo Arbeláez, Ross Girshick +1
Recognition algorithms based on convolutional networks (CNNs) typically use the output of the last layer as feature representation. However, the information in this layer may be to…
Deformable Part Models are Convolutional Neural Networks
Ross Girshick, Forrest Iandola, Trevor Darrell +1
Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition. They are typically viewed as distinct approaches: DPMs are…
Caffe: Convolutional Architecture for Fast Feature Embedding
Yangqing Jia, Evan Shelhamer, Jeff Donahue +5
Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The…
Learning Rich Features from RGB-D Images for Object Detection and Segmentation
Saurabh Gupta, Ross Girshick, Pablo Arbeláez +1
In this paper we study the problem of object detection for RGB-D images using semantically rich image and depth features. We propose a new geocentric embedding for depth images tha…
Part-based R-CNNs for Fine-grained Category Detection
Ning Zhang, Jeff Donahue, Ross Girshick +1
Semantic part localization can facilitate fine-grained categorization by explicitly isolating subtle appearance differences associated with specific object parts. Methods for pose-…
Simultaneous Detection and Segmentation
Bharath Hariharan, Pablo Arbeláez, Ross Girshick +1
We aim to detect all instances of a category in an image and, for each instance, mark the pixels that belong to it. We call this task Simultaneous Detection and Segmentation (SDS).…
LSDA: Large Scale Detection Through Adaptation
Judy Hoffman, Sergio Guadarrama, Eric Tzeng +5
A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories. Recently, deep convolutional neural networks (CNNs) hav…
Analyzing the Performance of Multilayer Neural Networks for Object Recognition
Pulkit Agrawal, Ross Girshick, Jitendra Malik
In the last two years, convolutional neural networks (CNNs) have achieved an impressive suite of results on standard recognition datasets and tasks. CNN-based features seem poised…
R-CNNs for Pose Estimation and Action Detection
Georgia Gkioxari, Bharath Hariharan, Ross Girshick +1
We present convolutional neural networks for the tasks of keypoint (pose) prediction and action classification of people in unconstrained images. Our approach involves training an…
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin, Michael Maire, Serge Belongie +7
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…
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
Forrest Iandola, Matt Moskewicz, Sergey Karayev +3
Convolutional Neural Networks (CNNs) can provide accurate object classification. They can be extended to perform object detection by iterating over dense or selected proposed objec…
On learning to localize objects with minimal supervision
Hyun Oh Song, Ross Girshick, Stefanie Jegelka +3
Learning to localize objects with minimal supervision is an important problem in computer vision, since large fully annotated datasets are extremely costly to obtain. In this paper…