• 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
Object Detection with Discriminatively Trained Part Based Models
We describe an object detection system based on mixtures of multiscale deformable part models. Our system is able to represent highly variable object classes and achieves state-of-the-art results inExpand
Face detection, pose estimation, and landmark localization in the wild
TLDR
It is shown that tree-structured models are surprisingly effective at capturing global elastic deformation, while being easy to optimize unlike dense graph structures, in real-world, cluttered images. Expand
A discriminatively trained, multiscale, deformable part model
TLDR
A discriminatively trained, multiscale, deformable part model for object detection, which achieves a two-fold improvement in average precision over the best performance in the 2006 PASCAL person detection challenge and outperforms the best results in the 2007 challenge in ten out of twenty categories. Expand
Articulated pose estimation with flexible mixtures-of-parts
  • Yi Yang, D. Ramanan
  • Computer Science
  • CVPR
  • 20 June 2011
TLDR
A general, flexible mixture model for capturing contextual co-occurrence relations between parts, augmenting standard spring models that encode spatial relations, and it is shown that such relations can capture notions of local rigidity. Expand
Articulated Human Detection with Flexible Mixtures of Parts
  • Yi Yang, D. Ramanan
  • Computer Science, Medicine
  • IEEE Transactions on Pattern Analysis and Machine…
  • 1 December 2013
TLDR
A general, flexible mixture model that jointly captures spatial relations between part locations and co-occurrence Relations between part mixtures, augmenting standard pictorial structure models that encode just spatial relations. Expand
Globally-optimal greedy algorithms for tracking a variable number of objects
TLDR
A near-optimal algorithm based on dynamic programming which runs in time linear in the number of objects andlinear in the sequence length is given which results in state-of-the-art performance. Expand
Learning to parse images of articulated bodies
TLDR
This work considers the machine vision task of pose estimation from static images, specifically for the case of articulated objects, and casts visual inference as an iterative parsing process, where one sequentially learns better and better features tuned to a particular image. Expand
Finding Tiny Faces
  • Peiyun Hu, D. Ramanan
  • Computer Science
  • IEEE Conference on Computer Vision and Pattern…
  • 13 December 2016
TLDR
The role of scale in pre-trained deep networks is explored, providing ways to extrapolate networks tuned for limited scales to rather extreme ranges and demonstrating state-of-the-art results on massively-benchmarked face datasets. Expand
Detecting activities of daily living in first-person camera views
TLDR
This work presents a novel dataset and novel algorithms for the problem of detecting activities of daily living in firstperson camera views, and develops novel representations including temporal pyramids and composite object models that exploit the fact that objects look different when being interacted with. Expand
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