Open AccessPosted Content
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
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TLDR
The creation of this benchmark dataset and the advances in object recognition that have been possible as a result are described, and the state-of-the-art computer vision accuracy with human accuracy is compared.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 images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions.
This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.read more
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PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action Understanding.
TL;DR: A new large scale benchmark (PKU-MMD) for continuous multi-modality 3D human action understanding and cover a wide range of complex human activities with well annotated information to benefit future researches on action detection for the community.
Proceedings Article
LSDA: Large Scale Detection through Adaptation
Judy Hoffman,Sergio Guadarrama,Eric Tzeng,Ronghang Hu,Jeff Donahue,Ross Girshick,Trevor Darrell,Kate Saenko +7 more
TL;DR: The Large Scale Detection through Adaptation (LSDA) algorithm as discussed by the authors learns the difference between the two tasks and transfers this knowledge to classifiers for categories without bounding box annotated data, turning them into detectors.
Journal ArticleDOI
FoodCam: A real-time food recognition system on a smartphone
Yoshiyuki Kawano,Keiji Yanai +1 more
TL;DR: This paper proposes a mobile food recognition system, FoodCam, the purposes of which are estimating calorie and nutrition of foods and recording a user’s eating habits and obtained positive evaluation by a user study compared to the food recording system without object recognition.
Posted Content
Are You Talking to a Machine? Dataset and Methods for Multilingual Image Question Answering
TL;DR: The mQA model, which is able to answer questions about the content of an image, is presented, which contains four components: a Long Short-Term Memory (LSTM), a Convolutional Neural Network (CNN), an LSTM for storing the linguistic context in an answer, and a fusing component to combine the information from the first three components and generate the answer.
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Universal representations:The missing link between faces, text, planktons, and cat breeds
Hakan Bilen,Andrea Vedaldi +1 more
TL;DR: With the advent of large labelled datasets and high-capacity models, the performance of machine vision systems has been improving rapidly, but the technology has still major limitations, starting from the fact that different vision problems are still solved by different models, trained from scratch or fine-tuned on the target data.
References
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Book ChapterDOI
Microsoft COCO: Common Objects in Context
Tsung-Yi Lin,Michael Maire,Serge Belongie,James Hays,Pietro Perona,Deva Ramanan,Piotr Dollár,C. Lawrence Zitnick +7 more
TL;DR: 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 by gathering images of complex everyday scenes containing common objects in their natural context.
Proceedings ArticleDOI
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
TL;DR: RCNN as discussed by the authors combines CNNs with bottom-up region proposals to localize and segment objects, and when labeled training data is scarce, supervised pre-training for an auxiliary task, followed by domain-specific fine-tuning, yields a significant performance boost.