• 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
  • 9,653
  • 1943
Scale-Space and Edge Detection Using Anisotropic Diffusion
A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as toExpand
  • 10,992
  • 1114
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
  • 1,578
  • 611
Graph-Based Visual Saliency
A new bottom-up visual saliency model, Graph-Based Visual Saliency (GBVS), is proposed. It consists of two steps: first forming activation maps on certain feature channels, and then normalizing themExpand
  • 3,066
  • 493
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
  • 2,192
  • 369
Pedestrian Detection: An Evaluation of the State of the Art
Pedestrian detection is a key problem in computer vision, with several applications that have the potential to positively impact quality of life. In recent years, the number of approaches toExpand
  • 2,292
  • 357
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
We propose a novel approach to learn and recognize natural scene categories. Unlike previous work, it does not require experts to annotate the training set. We represent the image of a scene by aExpand
  • 3,679
  • 293
Fast Feature Pyramids for Object Detection
Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detectionExpand
  • 1,429
  • 271
Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories
Current computational approaches to learning visual object categories require thousands of training images, are slow, cannot learn in an incremental manner and cannot incorporate prior informationExpand
  • 1,947
  • 258
Object class recognition by unsupervised scale-invariant learning
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constellations of parts. AExpand
  • 2,467
  • 221