15,838 research outputs found
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Beyond Intra-modality: A Survey of Heterogeneous Person Re-identification
An efficient and effective person re-identification (ReID) system relieves
the users from painful and boring video watching and accelerates the process of
video analysis. Recently, with the explosive demands of practical applications,
a lot of research efforts have been dedicated to heterogeneous person
re-identification (Hetero-ReID). In this paper, we provide a comprehensive
review of state-of-the-art Hetero-ReID methods that address the challenge of
inter-modality discrepancies. According to the application scenario, we
classify the methods into four categories -- low-resolution, infrared, sketch,
and text. We begin with an introduction of ReID, and make a comparison between
Homogeneous ReID (Homo-ReID) and Hetero-ReID tasks. Then, we describe and
compare existing datasets for performing evaluations, and survey the models
that have been widely employed in Hetero-ReID. We also summarize and compare
the representative approaches from two perspectives, i.e., the application
scenario and the learning pipeline. We conclude by a discussion of some future
research directions. Follow-up updates are avaible at:
https://github.com/lightChaserX/Awesome-Hetero-reIDComment: Accepted by IJCAI 2020. Project url:
https://github.com/lightChaserX/Awesome-Hetero-reI
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Pathological glomerulus classification plays a key role in the diagnosis of
nephropathy. As the difference between different subcategories is subtle,
doctors often refer to slides from different staining methods to make
decisions. However, creating correspondence across various stains is
labor-intensive, bringing major difficulties in collecting data and training a
vision-based algorithm to assist nephropathy diagnosis. This paper provides an
alternative solution for integrating multi-stained visual cues for glomerulus
classification. Our approach, named generator-to-classifier (G2C), is a
two-stage framework. Given an input image from a specified stain, several
generators are first applied to estimate its appearances in other staining
methods, and a classifier follows to combine visual cues from different stains
for prediction (whether it is pathological, or which type of pathology it has).
We optimize these two stages in a joint manner. To provide a reasonable
initialization, we pre-train the generators in an unlabeled reference set under
an unpaired image-to-image translation task, and then fine-tune them together
with the classifier. We conduct experiments on a glomerulus type classification
dataset collected by ourselves (there are no publicly available datasets for
this purpose). Although joint optimization slightly harms the authenticity of
the generated patches, it boosts classification performance, suggesting more
effective visual cues are extracted in an automatic way. We also transfer our
model to a public dataset for breast cancer classification, and outperform the
state-of-the-arts significantly.Comment: Accepted by AAAI 201
Context-aware Captions from Context-agnostic Supervision
We introduce an inference technique to produce discriminative context-aware
image captions (captions that describe differences between images or visual
concepts) using only generic context-agnostic training data (captions that
describe a concept or an image in isolation). For example, given images and
captions of "siamese cat" and "tiger cat", we generate language that describes
the "siamese cat" in a way that distinguishes it from "tiger cat". Our key
novelty is that we show how to do joint inference over a language model that is
context-agnostic and a listener which distinguishes closely-related concepts.
We first apply our technique to a justification task, namely to describe why an
image contains a particular fine-grained category as opposed to another
closely-related category of the CUB-200-2011 dataset. We then study
discriminative image captioning to generate language that uniquely refers to
one of two semantically-similar images in the COCO dataset. Evaluations with
discriminative ground truth for justification and human studies for
discriminative image captioning reveal that our approach outperforms baseline
generative and speaker-listener approaches for discrimination.Comment: Accepted to CVPR 2017 (Spotlight
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