• Publications
  • Influence
Geodesic flow kernel for unsupervised domain adaptation
TLDR
This paper proposes a new kernel-based method that takes advantage of low-dimensional structures that are intrinsic to many vision datasets, and introduces a metric that reliably measures the adaptability between a pair of source and target domains.
The pyramid match kernel: discriminative classification with sets of image features
  • K. Grauman, Trevor Darrell
  • Mathematics, Computer Science
    Tenth IEEE International Conference on Computer…
  • 17 October 2005
TLDR
A new fast kernel function is presented which maps unordered feature sets to multi-resolution histograms and computes a weighted histogram intersection in this space and is shown to be positive-definite, making it valid for use in learning algorithms whose optimal solutions are guaranteed only for Mercer kernels.
Relative attributes
TLDR
This work proposes a generative model over the joint space of attribute ranking outputs, and proposes a novel form of zero-shot learning in which the supervisor relates the unseen object category to previously seen objects via attributes (for example, ‘bears are furrier than giraffes’).
Kernelized locality-sensitive hashing for scalable image search
  • B. Kulis, K. Grauman
  • Mathematics, Computer Science
    IEEE 12th International Conference on Computer…
  • 1 December 2009
TLDR
It is shown how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sub-linear time similarity search guarantees for a wide class of useful similarity functions.
Key-segments for video object segmentation
TLDR
The method first identifies object-like regions in any frame according to both static and dynamic cues and compute a series of binary partitions among candidate “key-segments” to discover hypothesis groups with persistent appearance and motion.
Video Summarization with Long Short-Term Memory
TLDR
Long Short-Term Memory (LSTM), a special type of recurrent neural networks are used to model the variable-range dependencies entailed in the task of video summarization to improve summarization by reducing the discrepancies in statistical properties across those datasets.
Discovering important people and objects for egocentric video summarization
TLDR
This work introduced novel egocentric features to train a regressor that predicts important regions and produces significantly more informative summaries than traditional methods that often include irrelevant or redundant information.
Kernelized Locality-Sensitive Hashing
  • B. Kulis, K. Grauman
  • Mathematics, Medicine
    IEEE Transactions on Pattern Analysis and Machine…
  • 1 June 2012
TLDR
It is shown how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sublinear time similarity search guarantees for a wide class of useful similarity functions.
Fine-Grained Visual Comparisons with Local Learning
  • A. Yu, K. Grauman
  • Computer Science
    IEEE Conference on Computer Vision and Pattern…
  • 23 June 2014
TLDR
This work proposes a local learning approach for fine-grained visual comparisons that outperforms state-of-the-art methods for relative attribute prediction and shows how to identify analogous pairs using learned metrics.
Deformable Spatial Pyramid Matching for Fast Dense Correspondences
TLDR
This work introduces a fast deformable spatial pyramid (DSP) matching algorithm for computing dense pixel correspondences that simultaneously regularizes match consistency at multiple spatial extents-ranging from an entire image, to coarse grid cells, to every single pixel.
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