• Publications
  • Influence
Microsoft COCO: Common Objects in Context
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 sceneExpand
The Caltech-UCSD Birds-200-2011 Dataset
CUB-200-2011 is an extended version of CUB-200 [7], a challenging dataset of 200 bird species. The extended version roughly doubles the number of images per category and adds new part localizationExpand
Graph-Based Visual Saliency
TLDR
A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed, which powerfully predicts human fixations on 749 variations of 108 natural images, achieving 98% of the ROC area of a human-based control, whereas the classical algorithms of Itti & Koch achieve only 84%. Expand
Caltech-256 Object Category Dataset
We introduce a challenging set of 256 object categories containing a total of 30607 images. The original Caltech-101 [1] was collected by choosing a set of object categories, downloading examplesExpand
Pedestrian Detection: An Evaluation of the State of the Art
TLDR
An extensive evaluation of the state of the art in a unified framework of monocular pedestrian detection using sixteen pretrained state-of-the-art detectors across six data sets and proposes a refined per-frame evaluation methodology. Expand
A Bayesian hierarchical model for learning natural scene categories
  • Li Fei-Fei, P. Perona
  • Computer Science
  • IEEE Computer Society Conference on Computer…
  • 20 June 2005
TLDR
This work proposes a novel approach to learn and recognize natural scene categories by representing the image of a scene by a collection of local regions, denoted as codewords obtained by unsupervised learning. Expand
Fast Feature Pyramids for Object Detection
TLDR
For a broad family of features, this work finds that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid, and this approximation yields considerable speedups with negligible loss in detection accuracy. Expand
Self-Tuning Spectral Clustering
TLDR
This work proposes that a 'local' scale should be used to compute the affinity between each pair of points and suggests exploiting the structure of the eigenvectors to infer automatically the number of groups. Expand
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories
TLDR
The incremental algorithm is compared experimentally to an earlier batch Bayesian algorithm, as well as to one based on maximum-likelihood, which have comparable classification performance on small training sets, but incremental learning is significantly faster, making real-time learning feasible. Expand
Caltech-UCSD Birds 200
Caltech-UCSD Birds 200 (CUB-200) is a challenging image dataset annotated with 200 bird species. It was created to enable the study of subordinate categorization, which is not possible with otherExpand
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