208 research outputs found
Person Re-identification with Correspondence Structure Learning
This paper addresses the problem of handling spatial misalignments due to
camera-view changes or human-pose variations in person re-identification. We
first introduce a boosting-based approach to learn a correspondence structure
which indicates the patch-wise matching probabilities between images from a
target camera pair. The learned correspondence structure can not only capture
the spatial correspondence pattern between cameras but also handle the
viewpoint or human-pose variation in individual images. We further introduce a
global-based matching process. It integrates a global matching constraint over
the learned correspondence structure to exclude cross-view misalignments during
the image patch matching process, hence achieving a more reliable matching
score between images. Experimental results on various datasets demonstrate the
effectiveness of our approach
High Catalysis Activity of Cu<sub>2</sub>O Microcrystals to the Electrochemiluminesence of Luminol and H<sub>2</sub>O<sub>2</sub>
Learning Correspondence Structures for Person Re-identification
This paper addresses the problem of handling spatial misalignments due to
camera-view changes or human-pose variations in person re-identification. We
first introduce a boosting-based approach to learn a correspondence structure
which indicates the patch-wise matching probabilities between images from a
target camera pair. The learned correspondence structure can not only capture
the spatial correspondence pattern between cameras but also handle the
viewpoint or human-pose variation in individual images. We further introduce a
global constraint-based matching process. It integrates a global matching
constraint over the learned correspondence structure to exclude cross-view
misalignments during the image patch matching process, hence achieving a more
reliable matching score between images. Finally, we also extend our approach by
introducing a multi-structure scheme, which learns a set of local
correspondence structures to capture the spatial correspondence sub-patterns
between a camera pair, so as to handle the spatial misalignments between
individual images in a more precise way. Experimental results on various
datasets demonstrate the effectiveness of our approach.Comment: IEEE Trans. Image Processing, vol. 26, no. 5, pp. 2438-2453, 2017.
The project page for this paper is available at
http://min.sjtu.edu.cn/lwydemo/personReID.htm arXiv admin note: text overlap
with arXiv:1504.0624
Optimized Cartesian -Means
Product quantization-based approaches are effective to encode
high-dimensional data points for approximate nearest neighbor search. The space
is decomposed into a Cartesian product of low-dimensional subspaces, each of
which generates a sub codebook. Data points are encoded as compact binary codes
using these sub codebooks, and the distance between two data points can be
approximated efficiently from their codes by the precomputed lookup tables.
Traditionally, to encode a subvector of a data point in a subspace, only one
sub codeword in the corresponding sub codebook is selected, which may impose
strict restrictions on the search accuracy. In this paper, we propose a novel
approach, named Optimized Cartesian -Means (OCKM), to better encode the data
points for more accurate approximate nearest neighbor search. In OCKM, multiple
sub codewords are used to encode the subvector of a data point in a subspace.
Each sub codeword stems from different sub codebooks in each subspace, which
are optimally generated with regards to the minimization of the distortion
errors. The high-dimensional data point is then encoded as the concatenation of
the indices of multiple sub codewords from all the subspaces. This can provide
more flexibility and lower distortion errors than traditional methods.
Experimental results on the standard real-life datasets demonstrate the
superiority over state-of-the-art approaches for approximate nearest neighbor
search.Comment: to appear in IEEE TKDE, accepted in Apr. 201
What Can Simple Arithmetic Operations Do for Temporal Modeling?
Temporal modeling plays a crucial role in understanding video content. To
tackle this problem, previous studies built complicated temporal relations
through time sequence thanks to the development of computationally powerful
devices. In this work, we explore the potential of four simple arithmetic
operations for temporal modeling. Specifically, we first capture auxiliary
temporal cues by computing addition, subtraction, multiplication, and division
between pairs of extracted frame features. Then, we extract corresponding
features from these cues to benefit the original temporal-irrespective domain.
We term such a simple pipeline as an Arithmetic Temporal Module (ATM), which
operates on the stem of a visual backbone with a plug-andplay style. We conduct
comprehensive ablation studies on the instantiation of ATMs and demonstrate
that this module provides powerful temporal modeling capability at a low
computational cost. Moreover, the ATM is compatible with both CNNs- and
ViTs-based architectures. Our results show that ATM achieves superior
performance over several popular video benchmarks. Specifically, on
Something-Something V1, V2 and Kinetics-400, we reach top-1 accuracy of 65.6%,
74.6%, and 89.4% respectively. The code is available at
https://github.com/whwu95/ATM.Comment: Accepted by ICCV 202
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