Imagenet large scale visual recognition challenge
Abstract The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object
category classification and detection on hundreds of object categories and millions of …
category classification and detection on hundreds of object categories and millions of …
Deep fisher kernels-end to end learning of the fisher kernel gmm parameters
V Sydorov, M Sakurada… - Proceedings of the IEEE …, 2014 - cv-foundation.org
Abstract Fisher Kernels and Deep Learning were two developments with significant impact
on large-scale object categorization in the last years. Both approaches were shown to …
on large-scale object categorization in the last years. Both approaches were shown to …
Building high-level features using large scale unsupervised learning
QV Le - 2013 IEEE international conference on acoustics …, 2013 - ieeexplore.ieee.org
We consider the problem of building high-level, class-specific feature detectors from only
unlabeled data. For example, is it possible to learn a face detector using only unlabeled …
unlabeled data. For example, is it possible to learn a face detector using only unlabeled …
Objectnet: A large-scale bias-controlled dataset for pushing the limits of object recognition models
We collect a large real-world test set, ObjectNet, for object recognition with controls where
object backgrounds, rotations, and imaging viewpoints are random. Most scientific …
object backgrounds, rotations, and imaging viewpoints are random. Most scientific …
The Pascal Visual Object Classes (VOC) Challenge
M Everingham, L Van Gool, CKI Williams… - International journal of …, 2010 - Springer
Abstract The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object
category recognition and detection, providing the vision and machine learning communities …
category recognition and detection, providing the vision and machine learning communities …
The effect of improving annotation quality on object detection datasets: A preliminary study
J Ma, Y Ushiku, M Sagara - … of the IEEE/CVF Conference on …, 2022 - openaccess.thecvf.com
In this study, we partially reannotate conventional benchmark datasets for object detection
and check whether there is performance improvement/drop compared with the original …
and check whether there is performance improvement/drop compared with the original …
Dataset issues in object recognition
Appropriate datasets are required at all stages of object recognition research, including
learning visual models of object and scene categories, detecting and localizing instances of …
learning visual models of object and scene categories, detecting and localizing instances of …
LabelMe: a database and web-based tool for image annotation
We seek to build a large collection of images with ground truth labels to be used for object
detection and recognition research. Such data is useful for supervised learning and …
detection and recognition research. Such data is useful for supervised learning and …
Network of experts for large-scale image categorization
We present a tree-structured network architecture for large-scale image classification. The
trunk of the network contains convolutional layers optimized over all classes. At a given …
trunk of the network contains convolutional layers optimized over all classes. At a given …
Large-scale category structure aware image categorization
Most previous research on image categorization has focused on medium-scale data sets,
while large-scale image categorization with millions of images from thousands of categories …
while large-scale image categorization with millions of images from thousands of categories …