法國INRIA Data Sets & Images 資料集和影象庫
Data Sets & Images
Rome Patches
The dataset introduced in the Patch-CKN paper is available here.
Action Movie Franchises
Video alignment datasets
The datasets with temporally aligned video clips of a Climbing session and a Madonna concert, introduced in the arXiv paper Circulant temporal encoding for video retrieval and temporal alignmentYouTube Motion Boundaries dataset
A dataset with motion boundaries annotations, introduced in "Learning to Detect Motion Boundaries"(CVPR'15), is available here.
MED-Summaries dataset
A video summarization dataset, introduced in "Category-specific video summarization" (ECCV'14)
is available
Poses in the Wild Dataset
The dataset for evaluating human pose estimation in video sequences, introduced in our CVPR'14 paperMixing Body-Part Sequences for Human Pose Estimation, is available on the project page.
EVVE dataset
Youtube-Objects dataset
This dataset is composed of videos collected from YouTube by querying for the names of 10 object classes. It contains between 9 and 24 videos for each class, and can be downloaded from
Face Track Annotations Dataset
Actom annotations for action detection
Labeled Yahoo! News
This data set extends the Labeled Faces in the Wild data set. It consists of news documents composed of images and captions, we used it for face naming and learning face recognition systems with weak supervision in our ECCV 2010 paper and submitted IJCV paper. It is fully annotated for association of faces in the image with names in the caption.
"Web Queries" dataset
The labeled data set collected using image search engine. Contains 71478 images and text meta-data in XML format retrieved by 353 text queries, accompanied with relevance label for each image. This data set was used in our CVPR'10 paper Improving web-image search results using query-relative classifiers.
Web images for multiple query terms
INRIA Features for some data sets
INRIA features for the COREL 5K, IAPR TC-12, ESP GAME, PASCAL VOC 2007 and MIR Flickr data sets, as used in the ICCV 2009 paper on image auto-annotation and keyword-based retrieval and the CVPR 2010 paper on multimodal semi-supervised learning.
Image indexing database
Holidays dataset collected by Hervé Jégou et al. to test image search methods.
Hollywood Human Actions2
Hollywood Human Actions2 dataset. An extended version of our Hollywood Human Action dataset featuring more action classes and samples. The dataset was used in Actions in context published in CVPR'09.
Hollywood Human Actions
Hollywood Human Actions. A video dataset focusing on realistic human actions. Short video samples were retrieved from various popular movies and annotated both manually and automatically. The dataset was used in Learning Realistic Human Actions from Movies published in CVPR'08 paper (oral). The covered set of human actions includes answering a phone, getting out of a car, handshaking, hugging, kissing, sitting down, sitting up and standing up.
INRIA Annotations for Graz-02
INRIA Annotations for Graz-02. A follow-up on the popular natural-scene object category dataset prepared at Graz University of Technology. Original dataset images were re-annotated by a team of human annotators led by Marcin Marszalek, who then used the annotations to perform accurate object localization with shape masks (CVPR 2007 oral). All cars, bikes and people images, annotations and image lists are made available.
Color Names Data Sets
Color Names Data Sets. Two data sets collected for the automatic learning of color names as proposed inLearning Color Names from Real-World Images published in CVPR 2007.
Soccer Team Data Sets
Soccer Team Data Set. Data sets containing out of seven soccer teams. Has been used to evaluate various color descriptors on in Coloring Local Feature Extraction published in ECCV 2006.
Horse Data Set
Horse Dataset (navigate to INRIA horses). A set of horse and non-horse images collected by Frédéric Jurieand Vittorio Ferrari.
INRIA Person Data Set
INRIA Car Data Set
Interest Point Test Sequences
Test images collected by Krystian Mikolajczyk for testing scaled and affine interest point detectors with various types of local image descriptors.
from: http://lear.inrialpes.fr/data/