115,709 research outputs found
Data-Free Sketch-Based Image Retrieval
Rising concerns about privacy and anonymity preservation of deep learning
models have facilitated research in data-free learning (DFL). For the first
time, we identify that for data-scarce tasks like Sketch-Based Image Retrieval
(SBIR), where the difficulty in acquiring paired photos and hand-drawn sketches
limits data-dependent cross-modal learning algorithms, DFL can prove to be a
much more practical paradigm. We thus propose Data-Free (DF)-SBIR, where,
unlike existing DFL problems, pre-trained, single-modality classification
models have to be leveraged to learn a cross-modal metric-space for retrieval
without access to any training data. The widespread availability of pre-trained
classification models, along with the difficulty in acquiring paired
photo-sketch datasets for SBIR justify the practicality of this setting. We
present a methodology for DF-SBIR, which can leverage knowledge from models
independently trained to perform classification on photos and sketches. We
evaluate our model on the Sketchy, TU-Berlin, and QuickDraw benchmarks,
designing a variety of baselines based on state-of-the-art DFL literature, and
observe that our method surpasses all of them by significant margins. Our
method also achieves mAPs competitive with data-dependent approaches, all the
while requiring no training data. Implementation is available at
\url{https://github.com/abhrac/data-free-sbir}.Comment: Computer Vision and Pattern Recognition (CVPR) 202
Person Re-identification by Local Maximal Occurrence Representation and Metric Learning
Person re-identification is an important technique towards automatic search
of a person's presence in a surveillance video. Two fundamental problems are
critical for person re-identification, feature representation and metric
learning. An effective feature representation should be robust to illumination
and viewpoint changes, and a discriminant metric should be learned to match
various person images. In this paper, we propose an effective feature
representation called Local Maximal Occurrence (LOMO), and a subspace and
metric learning method called Cross-view Quadratic Discriminant Analysis
(XQDA). The LOMO feature analyzes the horizontal occurrence of local features,
and maximizes the occurrence to make a stable representation against viewpoint
changes. Besides, to handle illumination variations, we apply the Retinex
transform and a scale invariant texture operator. To learn a discriminant
metric, we propose to learn a discriminant low dimensional subspace by
cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is
learned on the derived subspace. We also present a practical computation method
for XQDA, as well as its regularization. Experiments on four challenging person
re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show
that the proposed method improves the state-of-the-art rank-1 identification
rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.Comment: This paper has been accepted by CVPR 2015. For source codes and
extracted features please visit
http://www.cbsr.ia.ac.cn/users/scliao/projects/lomo_xqda
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